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Outline

Title
Abstract
Figures
Background
Discussion of Contrabass Instruments
Computer Analysis and Synthesis
Analysis and Resynthesis Tasks
Analysis Without Resynthesis
Spectral Analysis
Analysis and Resynthesis
Overview
Discussion
Design of the Transcription System
Overall Design
Multirate Spectral Analysis
Octave Spectral Analysis
Summary of Osa
Application to Multirate Analysis
Transcription Results
Results
Interactive Analysis
Limitations of the Current Model
Introduction
Analysis Using Quanta
Analysis
Advantages for Analysis and Resynthesis
Summary
Conclusions
References
All Topics
Computer Science
Software

Analysis and resynthesis of polyphonic music

Profile image of Douglas NunnDouglas Nunn

1997

Abstract

This thesis examines applications of Digital Signal Processing to the analysis, transformation, and resynthesis of musical audio. First I give an overview of the human perception of music. I then examine in detail the requirements for a system that can analyse, transcribe, process, and resynthesise monaural polyphonic music. I then describe and compare the possible hardware and software platforms. After this I describe a prototype hybrid system that attempts to carry out these tasks using a method based on .. additive synthesis. Next I present results from its application to a variety of musical examples, and critically assess its pert:ormance and limitations. I then address these issues in the design of a second system based on Gabor wavelets. I conclude by summarising the research and outlining suggestions for future developments.

Figures (180)
A DISSERTATION SUBMITTED TO THE FACULTY OF SCIENCE IN CANDIDACY FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
A DISSERTATION SUBMITTED TO THE FACULTY OF SCIENCE IN CANDIDACY FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
None of this work has previously been submitted for a degree in this or any other university.
None of this work has previously been submitted for a degree in this or any other university.
This table summarises the distinctions between the definitions, ignoring definitions 1 and 6.
This table summarises the distinctions between the definitions, ignoring definitions 1 and 6.
printer noise shown in Figure 2 is in a twelve-tone scale and a fairly regular tempo — the low E is the
printer noise shown in Figure 2 is in a twelve-tone scale and a fairly regular tempo — the low E is the
the ‘hypersonic effect’, and was demonstrated using an Indonesian gamelan ensemble.* However, ultra
the ‘hypersonic effect’, and was demonstrated using an Indonesian gamelan ensemble.* However, ultra
The Ionian (major), Dorian, Phrygian, Lydian, Mixolydian, Aeolian (minor), and Locrian modes refer to
The Ionian (major), Dorian, Phrygian, Lydian, Mixolydian, Aeolian (minor), and Locrian modes refer to
Figure 9 - Responses of six fibres in the auditory nerve of a cat. [he CFs are roughly evenly distributed by pitch, although gammatone filters are a closer approximation.
Figure 9 - Responses of six fibres in the auditory nerve of a cat. [he CFs are roughly evenly distributed by pitch, although gammatone filters are a closer approximation.
processes. However, some studies’ °""” “ have pointed out that this mechanism could also form the
processes. However, some studies’ °""” “ have pointed out that this mechanism could also form the
This gives the following levels:- The sound power level is the total power emitted by a source in all directions. It is defined by:- reference pressure of 20 Pa. All logarithms are to base 10. The sound pressure level (SPL) is defined from the logarithm of the ratio of a sound’s pressure to a where Wo is one picowatt.
This gives the following levels:- The sound power level is the total power emitted by a source in all directions. It is defined by:- reference pressure of 20 Pa. All logarithms are to base 10. The sound pressure level (SPL) is defined from the logarithm of the ratio of a sound’s pressure to a where Wo is one picowatt.
values unless otherwise stated.
values unless otherwise stated.
Table 5 - Dynamic ranges of several instruments. a distance of 50 metres, and another singing pianissimo at 1 metre. The listener will judge the distar
Table 5 - Dynamic ranges of several instruments. a distance of 50 metres, and another singing pianissimo at 1 metre. The listener will judge the distar
nge, there are about 30 JNDs in one critical band. The table below summarises the results.'2”"*** The resolution is poorer at lower frequencies, and is never better than 2 Hz. Throughout most of th
nge, there are about 30 JNDs in one critical band. The table below summarises the results.'2”"*** The resolution is poorer at lower frequencies, and is never better than 2 Hz. Throughout most of th
Figure 13 - Inharmonicity of AO from Blackham’s data. tests by Schuck on a higher note, F1 (44 Hz), on a different piano, is shown in Figure 14.!°##°*"@™ § Data from tests by Blackham on the lowest note, AO (27.5 Hz), is shown in Figure 13, and data from
Figure 13 - Inharmonicity of AO from Blackham’s data. tests by Schuck on a higher note, F1 (44 Hz), on a different piano, is shown in Figure 14.!°##°*"@™ § Data from tests by Blackham on the lowest note, AO (27.5 Hz), is shown in Figure 13, and data from
The above has discussed frequency stretching, but another factor counteracts this. Given one tone and
The above has discussed frequency stretching, but another factor counteracts this. Given one tone and
Many, indeed most, other instruments also have formants. It has been suggested, in the context of a Stradivarius violin, that a third formant at the sum of the frequencies of the first two is an important
Many, indeed most, other instruments also have formants. It has been suggested, in the context of a Stradivarius violin, that a third formant at the sum of the frequencies of the first two is an important
{Licklider, Plomp 76, Van Klitzing, examples. Leman %] biomp concludes that the maximum effect i 18 Keyed brass are now rare; they include ophicleides and bugles.
{Licklider, Plomp 76, Van Klitzing, examples. Leman %] biomp concludes that the maximum effect i 18 Keyed brass are now rare; they include ophicleides and bugles.
Another case is when we hear a violin section playing in unison. Even if we know that there are sixteen have less musical importance and note decays are usually less abrupt. players, we cannot distinguish them, and hear it as a single but very complex instrument. Offset times are probably not detected with as much resolution as onset times because note durations a cymbal crash, for example
Another case is when we hear a violin section playing in unison. Even if we know that there are sixteen have less musical importance and note decays are usually less abrupt. players, we cannot distinguish them, and hear it as a single but very complex instrument. Offset times are probably not detected with as much resolution as onset times because note durations a cymbal crash, for example
f all the notes are C, E, and G, except for one G#, then change the G# to G”, then we might correct an
f all the notes are C, E, and G, except for one G#, then change the G# to G”, then we might correct an
However, transcription of polyphonic music is particularly difficult because before we can classifj
However, transcription of polyphonic music is particularly difficult because before we can classifj
bandwidth, and we wish to determine the frequencies involved. However, music covers at least ten
bandwidth, and we wish to determine the frequencies involved. However, music covers at least ten
For polyphonic music, this approach does not work due to the interference between notes, and a
For polyphonic music, this approach does not work due to the interference between notes, and a
individual peaks in the standard spectrogram, so the axis was tilted as shown in Figure 28. greyscales to different amplitude ranges, or by the contours of a spectrogram. It is difficult to pick out
individual peaks in the standard spectrogram, so the axis was tilted as shown in Figure 28. greyscales to different amplitude ranges, or by the contours of a spectrogram. It is difficult to pick out
3.1.3 Comparison of analysis tasks
3.1.3 Comparison of analysis tasks
e 29) after Risset!Risset 82] illustrates the framework for analysis and resynthesis
e 29) after Risset!Risset 82] illustrates the framework for analysis and resynthesis
break the waveform into its constituent musical entities, i.e. we must carry out source separation. sufficient redundancy to be coded more efficiently. To exploit this redundancy, however, we must first Representations based on events offer parallelism, compactness, and intuitiveness, but most of the theoretically impossible. A central hypothesis of this research is that any musical waveform contains
break the waveform into its constituent musical entities, i.e. we must carry out source separation. sufficient redundancy to be coded more efficiently. To exploit this redundancy, however, we must first Representations based on events offer parallelism, compactness, and intuitiveness, but most of the theoretically impossible. A central hypothesis of this research is that any musical waveform contains
generation, the following table shows how many operations are required per partial per sample Fre? ° suming linear interpolation of amplitude and frequency, and the use of a lookup table for sine A change in one actual controller, such as tongue-palette distance, will affect all of the harmonics, often
generation, the following table shows how many operations are required per partial per sample Fre? ° suming linear interpolation of amplitude and frequency, and the use of a lookup table for sine A change in one actual controller, such as tongue-palette distance, will affect all of the harmonics, often
and thus detract from the generality of an analysis method. It is easy to form artificial tones that do not [t should, however, be remembered that these assumptions may not apply to non-acoustic instruments (arson yi] Other techniques include the phase vocoder, which is often used for speech , and linear
and thus detract from the generality of an analysis method. It is easy to form artificial tones that do not [t should, however, be remembered that these assumptions may not apply to non-acoustic instruments (arson yi] Other techniques include the phase vocoder, which is often used for speech , and linear
Table 14 - Specifications of PCs. Dan Technology, and ‘CMC’ is Cambridge MicroComputers. The PC experiments were performed on one of three machines, summarised in the table below. ‘Dan’ is
Table 14 - Specifications of PCs. Dan Technology, and ‘CMC’ is Cambridge MicroComputers. The PC experiments were performed on one of three machines, summarised in the table below. ‘Dan’ is
Figure 32. order to investigate the possibilities of real-time synthesis. The hardware is shown schematically i Towards the end of this work, an output board for the C40 was designed and made by Milos Kolar in
Figure 32. order to investigate the possibilities of real-time synthesis. The hardware is shown schematically i Towards the end of this work, an output board for the C40 was designed and made by Milos Kolar in
The DAC buffering is shown in Figure 35, and the DAC subsystem is shown in Figure 36.
The DAC buffering is shown in Figure 35, and the DAC subsystem is shown in Figure 36.
The theoretical computational power and other features of the four systems are as follows:- The task we are undertaking — the analysis, transcription, transformation, and resynthesis of polyphonic
The theoretical computational power and other features of the four systems are as follows:- The task we are undertaking — the analysis, transcription, transformation, and resynthesis of polyphonic
illustrated in Figure 37. This was later extended to analysis of synthesised musical sounds !8°°w"s Figure 37 - Overview of Guy Brown’s analysis system. was designed for separating speech from the other sources described above /BrownS 92a, BrownG 4b] 7, i.
illustrated in Figure 37. This was later extended to analysis of synthesised musical sounds !8°°w"s Figure 37 - Overview of Guy Brown’s analysis system. was designed for separating speech from the other sources described above /BrownS 92a, BrownG 4b] 7, i.
In his system, the front-end analysis provides several outputs:- an overall signal intensity, a set o: wregman illusion, described in the previous chapter. " Ellis uses the continuity illusion to back up his case
In his system, the front-end analysis provides several outputs:- an overall signal intensity, a set o: wregman illusion, described in the previous chapter. " Ellis uses the continuity illusion to back up his case
Other research not reviewed here includes work by Watson at Sydney", Heinbach at
Other research not reviewed here includes work by Watson at Sydney", Heinbach at
performance analysis. In many cases the aims fall into several categories. [he table below summarises most of the polyphonic systems above. EPA stands for expressive
performance analysis. In many cases the aims fall into several categories. [he table below summarises most of the polyphonic systems above. EPA stands for expressive
filtering and FFTs. Eventually, these may be on separate processors. analyser task handles the computation — in this case the Octave Spectral Analysis which includes
filtering and FFTs. Eventually, these may be on separate processors. analyser task handles the computation — in this case the Octave Spectral Analysis which includes
The perfect reconstruction property implies that sharp filters are not an absolute necessity. However, it
The perfect reconstruction property implies that sharp filters are not an absolute necessity. However, it
gn Package." ” Figure 43 shows the frequency response, and Figure 44 shows detail of the band The FIR filter (FIRK6.FLT) used has a length of 255. It was designed using DFDP, the Digital Filter
gn Package." ” Figure 43 shows the frequency response, and Figure 44 shows detail of the band The FIR filter (FIRK6.FLT) used has a length of 255. It was designed using DFDP, the Digital Filter
6.4.6 Filter timing The filter length of 255 means that there is a group delay of 128 samples. However, since the samp
6.4.6 Filter timing The filter length of 255 means that there is a group delay of 128 samples. However, since the samp
Within each octave, the processing load fluctuates with time. When the FFT buffer is full, the FFT is 6.4.8 FFT buffering
Within each octave, the processing load fluctuates with time. When the FFT buffer is full, the FFT is 6.4.8 FFT buffering
Figure 48 - Logarithmic spectrogram of 30 seconds of Mendelssohn's Sonata 3 for Organ.
Figure 48 - Logarithmic spectrogram of 30 seconds of Mendelssohn's Sonata 3 for Organ.
The next program, called DISTRIBx®?, also reads the files produced by the Octave Spectral Analysis procedure is either imprecise or computationally expensive, or somewhere in-between, in that there is no quick way to calculate it accurately, and several ways of approximating the real spectrum, such as AR
The next program, called DISTRIBx®?, also reads the files produced by the Octave Spectral Analysis procedure is either imprecise or computationally expensive, or somewhere in-between, in that there is no quick way to calculate it accurately, and several ways of approximating the real spectrum, such as AR
discussion, all frequencies in the following paragraph are normalised with respect to the analysis
discussion, all frequencies in the following paragraph are normalised with respect to the analysis
First, a program was developed to test this deconvolution procedure. The test data was much simpler
First, a program was developed to test this deconvolution procedure. The test data was much simpler
The first diagram above shows the effect of the Blackman-Harris weighting; the peaks are much
The first diagram above shows the effect of the Blackman-Harris weighting; the peaks are much
by time using a program called TRAKSIN«x to give longer entities called chains- These are similar to the ; elements’ of Brown.6f¥"S 1 This is done by linking sines in neighbouring blocks using simple birth-death model as shown in Figure 54. Note that sinusoids are only linked at their ends, s frequently. It is possible for a sinusoid
by time using a program called TRAKSIN«x to give longer entities called chains- These are similar to the ; elements’ of Brown.6f¥"S 1 This is done by linking sines in neighbouring blocks using simple birth-death model as shown in Figure 54. Note that sinusoids are only linked at their ends, s frequently. It is possible for a sinusoid
threshold. The resultant sets of linked sines are
threshold. The resultant sets of linked sines are
the sines into virtual memory. It then reads the chain file and outputs the tracks of linked sines (a 59. Since music relies on near-integer frequency ratios, we expect the harmonics of different notes to overlap. Thus, chains can be claimed several times, in which case the total amplitude is shared between
the sines into virtual memory. It then reads the chain file and outputs the tracks of linked sines (a 59. Since music relies on near-integer frequency ratios, we expect the harmonics of different notes to overlap. Thus, chains can be claimed several times, in which case the total amplitude is shared between
First, all of the data is read into virtual memory. We then carry out an exhaustive search, looking for simultaneous frequencies that are
First, all of the data is read into virtual memory. We then carry out an exhaustive search, looking for simultaneous frequencies that are
that more harmonics are not sought is mainly due to memory restrictions. We match up to the 16" harmonic, and the resultant note groups are as shown in F igure 60. The reason
that more harmonics are not sought is mainly due to memory restrictions. We match up to the 16" harmonic, and the resultant note groups are as shown in F igure 60. The reason
about ‘typical’ instrument spectra. After this, we try to smooth the amplitude envelopes of each harmonic by adjusting the strengths of the
about ‘typical’ instrument spectra. After this, we try to smooth the amplitude envelopes of each harmonic by adjusting the strengths of the
Disk buffer (words) Table 18 - Dependence of OSA timing on disk and FFT buffer sizes.
Disk buffer (words) Table 18 - Dependence of OSA timing on disk and FFT buffer sizes.
workings of the analysis system. These form (((audio examples 1 and 2))) The pieces were around half a that real-time performance marginally not possible, but could possibly be achieved using shorter filters.
workings of the analysis system. These form (((audio examples 1 and 2))) The pieces were around half a that real-time performance marginally not possible, but could possibly be achieved using shorter filters.
Table 21 - Parameters of windows used in analysis. and the values are given in the following table. W(i)=ao-a) *cos(27*i/N)+a2*cos(2*272*i/N)-a3*cos(3 *22*i/N)
Table 21 - Parameters of windows used in analysis. and the values are given in the following table. W(i)=ao-a) *cos(27*i/N)+a2*cos(2*272*i/N)-a3*cos(3 *22*i/N)
analyses is periodic, as illustrated in Figure 69. block contains high frequencies. This is due to the fundamental assumption of the FFT that the block i
analyses is periodic, as illustrated in Figure 69. block contains high frequencies. This is due to the fundamental assumption of the FFT that the block i
Figure 71 - Hamming window. Figure 72 - Blackman 2-term window.
Figure 71 - Hamming window. Figure 72 - Blackman 2-term window.
Figure 73 - Blackman minimum 4-term window.
Figure 73 - Blackman minimum 4-term window.
durations of the notes are shown below.
durations of the notes are shown below.
The first five notes examined the effect of changing the duration of the note while the frequency and to be a proportionately weaker component. We could expect this to affect the third, fourth, and fifth
The first five notes examined the effect of changing the duration of the note while the frequency and to be a proportionately weaker component. We could expect this to affect the third, fourth, and fifth
included in 2 blocks if it falls over a boundary. At this stage, it is informative to consider what are the shortest and lowest notes that are likely to occur.
included in 2 blocks if it falls over a boundary. At this stage, it is informative to consider what are the shortest and lowest notes that are likely to occur.
illustrated in Figure 76.
illustrated in Figure 76.
Figure 77 - Results of deconvolution for eight thresholds.
Figure 77 - Results of deconvolution for eight thresholds.
SHOWTRX for MTest2 for the eight deconvolution thresholds.
SHOWTRX for MTest2 for the eight deconvolution thresholds.
Figure 78 - Tracked partials for eight thresholds.
Figure 78 - Tracked partials for eight thresholds.
of 6 bits is equivalent to a halving of amplitude.
of 6 bits is equivalent to a halving of amplitude.
sines and 405 tracks. 7.2.2.8 Recognition of quiet notes
sines and 405 tracks. 7.2.2.8 Recognition of quiet notes
have ONE partial — i.e. the fundamental. The original score and the derived scores are shown in Figure 80. This diagram (for the -24 dB
have ONE partial — i.e. the fundamental. The original score and the derived scores are shown in Figure 80. This diagram (for the -24 dB
frequency to a twelve-tone set and would be unsuitable for comparing notes with glissandi. holes we would see if we held two piano rolls together. It is noted that this assumes a quantisation of
frequency to a twelve-tone set and would be unsuitable for comparing notes with glissandi. holes we would see if we held two piano rolls together. It is noted that this assumes a quantisation of
sufficient to suggest a valid note.
sufficient to suggest a valid note.
Table 28 - Quantitative scores for MTest2 recognition. these categories”4, and these are used to calculate the final ‘accuracy’ figure. The outputs for MTest2 can be summarised in the following table. It shows the total number of notes in
Table 28 - Quantitative scores for MTest2 recognition. these categories”4, and these are used to calculate the final ‘accuracy’ figure. The outputs for MTest2 can be summarised in the following table. It shows the total number of notes in
example 3))). All audio examples are listed in Appendix Q. It will be noted that the current system doe 345 [Mendelssohn] THe audio was created in mono 16-bit linear format at 32 kHz using Csound files
example 3))). All audio examples are listed in Appendix Q. It will be noted that the current system doe 345 [Mendelssohn] THe audio was created in mono 16-bit linear format at 32 kHz using Csound files
[Rossing] the bass and sub-bass clefs.
[Rossing] the bass and sub-bass clefs.
Figure 87 - Battle output for Mendelssohn.
Figure 87 - Battle output for Mendelssohn.
hoped-for) notes, dark grey is predicted notes, and the overlap is in black. Next we judge the results using the diagram shown in Figure 89. This shows the overall amount of false The scores are compared using READASC, as shown in Figure 88. As before, light grey is expected (or positives (extra notes), correct identifications, and false negatives (missed notes). after three iterations. In this figure, time runs from top to bottom and frequency runs from left to right.
hoped-for) notes, dark grey is predicted notes, and the overlap is in black. Next we judge the results using the diagram shown in Figure 89. This shows the overall amount of false The scores are compared using READASC, as shown in Figure 88. As before, light grey is expected (or positives (extra notes), correct identifications, and false negatives (missed notes). after three iterations. In this figure, time runs from top to bottom and frequency runs from left to right.
This analysis was repeated for other deconvolution thresholds. Thresholds of -6, -12, and -18 dB gave Figure 90 - Comparison of scores for thresholds of -24/30/36/42 dB.
This analysis was repeated for other deconvolution thresholds. Thresholds of -6, -12, and -18 dB gave Figure 90 - Comparison of scores for thresholds of -24/30/36/42 dB.
no output — below are the compared scores for -24, -30, -36, and -42 dB.
no output — below are the compared scores for -24, -30, -36, and -42 dB.
Table 31 - Comparison of comparison methods. neither evaluated or conveyed. There are many different types of error. Notes added from harmonics
Table 31 - Comparison of comparison methods. neither evaluated or conveyed. There are many different types of error. Notes added from harmonics
captures many of the details are recognisable, even for the relatively high polyphony.
captures many of the details are recognisable, even for the relatively high polyphony.
Deconvolution was tried at various thresholds, but none of them was able to even remotely identify the trombone 118, for a total of 511. The score (in C) of the short extract above is shown in Figure 96.
Deconvolution was tried at various thresholds, but none of them was able to even remotely identify the trombone 118, for a total of 511. The score (in C) of the short extract above is shown in Figure 96.
It was sampled from CD and converted to mono with a sample rate of 32000 Hz. The test for this piece was that the pianist was known to be one of the 28 mentioned in a previous study on expressive
It was sampled from CD and converted to mono with a sample rate of 32000 Hz. The test for this piece was that the pianist was known to be one of the 28 mentioned in a previous study on expressive
As the aim was deduction of timing information, a smaller FFT, of size 32, was used.
As the aim was deduction of timing information, a smaller FFT, of size 32, was used.
notes will rub each other out problem arises with the display: the colours are exclusive-or’ed on screen. This means that two identical
notes will rub each other out problem arises with the display: the colours are exclusive-or’ed on screen. This means that two identical
a note to 50 milliseconds. The results for several thresholds are shown below. limits the number picked per octave to 6. The battle used the flag -t 0.05 to set the minimum length
a note to 50 milliseconds. The results for several thresholds are shown below. limits the number picked per octave to 6. The battle used the flag -t 0.05 to set the minimum length
not been recognised. Some of the descending line, and some later chords, have been captured, but most of the details have
not been recognised. Some of the descending line, and some later chords, have been captured, but most of the details have
latter, we can ask how a computer model could be constructed so as to far exceed the capabilities of our individual violins’? If the former, we can ask how violins and other instruments fuse together; if perception of ‘the sound of a violin section’, or is it that which separates this into ‘the sounds of sixteen Again, the transcription is poor. As the ‘correct’ score is not known, it is not possible to quantify the difficulty in the definition of the transcription task — is the correct transcription that which models our
latter, we can ask how a computer model could be constructed so as to far exceed the capabilities of our individual violins’? If the former, we can ask how violins and other instruments fuse together; if perception of ‘the sound of a violin section’, or is it that which separates this into ‘the sounds of sixteen Again, the transcription is poor. As the ‘correct’ score is not known, it is not possible to quantify the difficulty in the definition of the transcription task — is the correct transcription that which models our
minimum note length of 0.5 seconds. The average level is -15.51 dB. The analysis was done using Pickout (v0.24) with a threshold of -24 dB
minimum note length of 0.5 seconds. The average level is -15.51 dB. The analysis was done using Pickout (v0.24) with a threshold of -24 dB
It is very simple to form the ideal score. The quantitative evaluation is as follows:- expected because while the didgeridoo is a lip-reed instrument, it has a cylindrical bore and should thus from the spectrum, the odd harmonics are considerably stronger than the even harmonics. This is to be
It is very simple to form the ideal score. The quantitative evaluation is as follows:- expected because while the didgeridoo is a lip-reed instrument, it has a cylindrical bore and should thus from the spectrum, the odd harmonics are considerably stronger than the even harmonics. This is to be
analysis of the above short segment. The results.are shown below. However, some of the modes are nearly harmonic, so it is informative to see if these are sufficient, by
analysis of the above short segment. The results.are shown below. However, some of the modes are nearly harmonic, so it is informative to see if these are sufficient, by
The output score for a threshold of -24 dB is shown in Figure 112. It shows that the descending scale
The output score for a threshold of -24 dB is shown in Figure 112. It shows that the descending scale
Figure 115 - Very low, low, average, high, and very high frequencies. convenient but inaccurate ‘scientific tuning’ where middle C has a frequency of 256 Hz rather than 261
Figure 115 - Very low, low, average, high, and very high frequencies. convenient but inaccurate ‘scientific tuning’ where middle C has a frequency of 256 Hz rather than 261
an atom of four quanta with different times but the same f, a, and m. The notation for quanta is extended using braces such that, for example, Q({to,t;,t2,t3},f,a,m) represents
an atom of four quanta with different times but the same f, a, and m. The notation for quanta is extended using braces such that, for example, Q({to,t;,t2,t3},f,a,m) represents
When two atoms are multiplied, the resultant species is given by the following table An easier way to express this is:- 8.6.2.3 Multiplication and convolution
When two atoms are multiplied, the resultant species is given by the following table An easier way to express this is:- 8.6.2.3 Multiplication and convolution
frequency is effectively lowered.85 Roads also discusses FIR filtering by convolution.!"°*s *
frequency is effectively lowered.85 Roads also discusses FIR filtering by convolution.!"°*s *
Conventional amplitude modulation of a signal can be expressed as multiplication of the input quanta by
Conventional amplitude modulation of a signal can be expressed as multiplication of the input quanta by
These too can be made using a set of impulses regularly spaced in the /og-time domain. the density, which should be infinite to avoid frequency distortion, as shown in Figure 131. what we might call a Shephard rhythm, which incessantly slows down while adding faster beats. Risset § Reverberation is the same as echoes except the sound Whereas Shephard tones. have an incessantly rising time but a constant pitch height, Risset demonstrates is diffuse. This can be modelled as above using a large
These too can be made using a set of impulses regularly spaced in the /og-time domain. the density, which should be infinite to avoid frequency distortion, as shown in Figure 131. what we might call a Shephard rhythm, which incessantly slows down while adding faster beats. Risset § Reverberation is the same as echoes except the sound Whereas Shephard tones. have an incessantly rising time but a constant pitch height, Risset demonstrates is diffuse. This can be modelled as above using a large
In non-real-time situations, it is just as easy to create non-causal effects, achieved by convolution with a
In non-real-time situations, it is just as easy to create non-causal effects, achieved by convolution with a
87 A pedal note is one played using the lowest mode of vibration of a brass instrument. An example is shown in Figure 139; it represents a basic melody with no variation in magnitude. In species 12, shown in Figure 138, all the quanta share a magnitude and an alfa. replace it by a set of quanta summing to a rectangular pulse.
87 A pedal note is one played using the lowest mode of vibration of a brass instrument. An example is shown in Figure 139; it represents a basic melody with no variation in magnitude. In species 12, shown in Figure 138, all the quanta share a magnitude and an alfa. replace it by a set of quanta summing to a rectangular pulse.
This species can represent a melodic line, as in Figure 141, although the quanta have the same density. Species 13 has only one density, and is illustrated in Figure 140.
This species can represent a melodic line, as in Figure 141, although the quanta have the same density. Species 13 has only one density, and is illustrated in Figure 140.
$.6.5.16.1 MIDI atoms The notes in a MIDI file can easily be ‘converted to’ an atom of species 15.
$.6.5.16.1 MIDI atoms The notes in a MIDI file can easily be ‘converted to’ an atom of species 15.
instruments were found to display formant-like behaviour. (Although Moorer claims they do not." °°"
instruments were found to display formant-like behaviour. (Although Moorer claims they do not." °°"
The construction of larger musical structures is illustrated below. I show the musical intention and the
The construction of larger musical structures is illustrated below. I show the musical intention and the
8.9 Analysis using quanta individually to the DAC, we can only manage 87 in real time. Thus, if buffering is implemented we cat
8.9 Analysis using quanta individually to the DAC, we can only manage 87 in real time. Thus, if buffering is implemented we cat
short and long spectral details. Although the complete analysis/resynthesis system has not yet been full analysis as synthesis. Quanta can be used throughout the analysis scheme; they are applicable to |
short and long spectral details. Although the complete analysis/resynthesis system has not yet been full analysis as synthesis. Quanta can be used throughout the analysis scheme; they are applicable to |
Xc are intermediate results, and Xs is the spectrum data. The general flow diagram for the radix-2 decimation-in-time FFT is shown in Figure 150 for | order that this can be calculated with O(logyN) operations each sample, we must distort each of the
Xc are intermediate results, and Xs is the spectrum data. The general flow diagram for the radix-2 decimation-in-time FFT is shown in Figure 150 for | order that this can be calculated with O(logyN) operations each sample, we must distort each of the
Table 44 - Algorithm for smoothed FFT. The algorithm for N=8 is as follows:-
Table 44 - Algorithm for smoothed FFT. The algorithm for N=8 is as follows:-
convolved with a periodic input spectrum
convolved with a periodic input spectrum
The first term here indicates the linear phase shift, and the second is the amplitude response, showing how each frequency leaks into neighbouring bins in the DFT.
The first term here indicates the linear phase shift, and the second is the amplitude response, showing how each frequency leaks into neighbouring bins in the DFT.
10.10 Appendix J — Terms | define Table 47 - Terms I define in this thesis.
10.10 Appendix J — Terms | define Table 47 - Terms I define in this thesis.
performer played the upbeat literally but delayed the chord. By comparing this to the first panel ot
performer played the upbeat literally but delayed the chord. By comparing this to the first panel ot
The BiMouse is made using two standard and ideally identical mice, as shown in Figure 155. Wt 9! Four serial ports is normally the maximum on a PC — most systems have only two ports. However, an interesting installation by Okamoto at the 1996 ICMC used specially-designed hardware to allow sixteen mice to control real-time polyphonic synthesis (Ok#motl
The BiMouse is made using two standard and ideally identical mice, as shown in Figure 155. Wt 9! Four serial ports is normally the maximum on a PC — most systems have only two ports. However, an interesting installation by Okamoto at the 1996 ICMC used specially-designed hardware to allow sixteen mice to control real-time polyphonic synthesis (Ok#motl
This shows that the absolute error does not exceed 2-16 when we truncate the end of the series First we try to form
This shows that the absolute error does not exceed 2-16 when we truncate the end of the series First we try to form
Table 48 - List of audio examples. URL http://capella.dur.ac.uk/doug/thesis/.
Table 48 - List of audio examples. URL http://capella.dur.ac.uk/doug/thesis/.
La réallocation: une méthode générale d'amélioration de la lisibilité des représentation temps-fréquence bilinéaires
La réallocation: une méthode générale d'amélioration de la lisibilité des représentation temps-fréquence bilinéaires

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References (472)

  1. E-di1: - :: ~ --~ .i ;
  2. J. .. t v~ L u ~ ~ ~ • T I' -a~""-'-"" r «--.' . 11u L 1: i • 1 ~ C"'n..,.O.L..,~ S"' r-1: ;
  3. C'\l>~tJb. ;-nc•- Inri.i.l~ '\l>U1: Ti-d~,u• 11. References
  4. Amuedo J. Amuedo, Periodicity estimation by hypothesis-directed search, Proc. ICASSP, Tampa, Florida, 395-398, 1985
  5. Analog
  6. Baines Analog Devices, ADSP-21 020 User's Manual, 1991
  7. American National Standards Institute, American Standard Acoustical Terminology, S I. 1-1960, 1960
  8. D. Arfib, In the intimacy of a sound, Proc. ICMC, Glasgow, 43-45, 1990
  9. D. Arfib, Analysis, transformation, and resynthesis of musical sounds with the help of a time-frequency representation, in G. De Poli, A Piccialli, and C. Roads, eds., Representations of Musical Signals, Cambridge, Massachusetts: MIT Press, 1991 .. .
  10. F. Armani, A. Paladin, and C. Rosati, MARS Applications Using APPL120 Development Tools: a Case Study, Proc. ICMC, Aarhus, 230-236, 1994
  11. P.F. Assmann and Q. Summerfield, Modeling the perception of concu"ent vowels: Vowels with the same fundamental frequency, JASA, 85, 327-338, 1989 P.F. Assmann and Q. Summerfield, Modeling the perception of concu"ent vowels: Vowels with different fundamental frequencies, JASA, 88, 680-697, 1990
  12. Athena Consulting, Athena Consulting Stock Answers: How FAST are Athena workstations?, <http:// mufasa.mit.edu/ stock_answers/ workstations/ ws_speeds.html>, 27 September 1996
  13. Atlanta Signal Processors Inc., Digital Filter Design Package, <http:/ /www.aspi.com/techsprt/dfdp.htm>, November 1996
  14. AudioWorks Ltd., Sound2Midi Info (sales literature), <http:// www.audioworks.com/s2m.htm>, 23 June 96
  15. F. Auger and G. Flandrin, La reallocation: une methode generate d'ame/ioration de Ia /isibi/ite des representation temps-frequence bilineaires, Proc. Colloque Temps-Frequence, Ondelettes et Multiresolution: Theorie, Modeles et Applications, Lyon, France, 15.1-15.7, 1994 [in French]
  16. J. Backus, Input impedance curves for the brass instruments, JASA, 60(2), 470-480, August 1976
  17. N. Bailey, A. Purvis, I. Bowler, and P.D. Manning, An highly parallel architecture for real-time music synthesis and digital signal processing applications, Proc. ICMC, Glasgow, 167-171, 1990
  18. N. Bailey, On the synthesis and processing of high-quality audio signals by parallel computers, PhD thesis, Durham Univ., 1991
  19. A. Baines, Brass Instruments, London: Faber & Faber, 1976
  20. Baird Balzano Baraniuk Bargar 92 Bargar 95
  21. BergerK B. Baird, D. Blevins, and N. Zahler, Artificial intelligence and music: implementing an interactive computer performer, CMJ, 17(2), 73-79, Summer 1993 G.J. Balzano, The Group-theoretic Description of 12-Fold and Microtonal Pitch Systems, CMJ, 4(4), 66-84, Winter 1980
  22. R.G. Baraniuk and D.L. Jones, Shear Madness: new orthonormal bases and frames using chirp functions, IEEE Trans. Signal Processing, 41(12), 3543- 3548, December 1993, and <http://www-dsp.rice.edu/publicationslpub/ shear_ madness. ps.Z>
  23. R. Bargar, Correlated sound and image in a digital medium, Proc. ICMC, San Jose, 194-197, 1992
  24. R. Bargar, B. Holloway, X. Rodet, and C. Hartman, Defining Spectral Surfaces, Proc. ICMC, Banff, 373-376, 1995
  25. T. Bartoo and B. Truax, Electro-Acoustic Composer's Workstation Project, Proc. ICMC, San Jose, 446, 1992
  26. M.J. Bastiaans, Gabor's Expansion of a Signal into Gaussian Elementary Signals, Proc. IEEE, 68(4), 538-539, April1980
  27. M.J. Bastiaans, On the sliding-window representation in digital signal processing, IEEE Trans. ASSP, 33(4), 1985
  28. J. Beauchamp, A Computer System for Time-Variant Harmonic Analysis and Synthesis of Musical Tones, in Music By Computers, H. von Foerster and J. Beauchamp, eds., New York: John Wiley & Sons, 1969
  29. A.J. Bell and T.J Sejnowski, An information-maximisation approach to blind separation and blind deconvolution, Neural Computation, 7, 1129-1159, 1995, and <ftp://ftp. cnl. salk.edu/pub/tony/bell. blind. ps.Z>
  30. M. Bellanger, J. Daguet, and G. Lepagnol, Interpolation extrapolation and reduction of computation speed in digital filters, IEEE Trans. ASSP, 22, 231- 235, August 1974
  31. A.H. Benade and J.W. French, Analysis of the Flute Head Joint, JASA, 37, 679-691, 1965
  32. A.H. Benade, The Physics of Brasses, Scientific American, 229(1), 24-35, July 1973, reprinted in Musical Acoustics-Piano and Wind Instruments, E.L. Kent, ed., Stroudsburg, Pennsylvania: Dowden, Hutchinson and Ross, 1977
  33. J. Berger, R.R. Coifman, and M.J. Goldberg, A Method of Denoising and Reconstructing Audio Signals, Proc. ICMC, Aarhus, 344-347, 1994
  34. J. Berger and C. Nichols, Using Wavelet Based Analysis and Resynthesis to Uncover the Past, Proc. ICMC, Aarhus, 352-355, 1994
  35. J. Berger, R.R. Coifman, and M.J. Goldberg, Removing Noise from Music Using Local Trigonometric Bases and Wavelet Packets, J. AES, 42(10), 808- 818, 1994
  36. J. Berger, R.R. Coifman, and M.J. Goldberg, A two-stage automatic adaptive process to remove noise from an audio signal, Proc. ICMC, Banff, 288-291, 1995, and <http://www.music.yale.edu/research/denoise/ denoise2.html> K.W. Berger, Some Factors in the Recognition of Timbre, JASA, 36, 1888, 1963
  37. BrownG96 C. Bevan, The Tuba Family, London: Faber & Faber, 1978
  38. J.W. Birdsall <support@picarefy.com>, XMSIF, <ftp://sunsite.doc.ic.ac.uk/ packages/simtelnet/msdos/c/xmsifl5.zip>, 1994
  39. E.D. Blackham, The Physics of the Piano, in Musical Acoustics -Piano and Wind Instruments, E.L. Kent, ed., Stroudsburg, Pennsylvania: Dowden, Hutchinson and Ross, 1977
  40. J.J. Bloch and R.B. Dannenberg, Real-Time Computer Accompaniment of Keyboard Performances, Proc. ICMC, ?, 279-290, 1985
  41. N. Boatin, G. De Poli, and P. Prandoni, Timbre Characterization with Mei- Cepstrum: a Multivariate Analysis, Proc. XI Colloquium on Musical Informatics, Bologna, 145-148, 1995
  42. Borland Inc., Turbo C Reference Guide, Scotts Valley: Borland, 1988
  43. M. Bosi, The Sound Accelerator as a Real-Time DSP Environment: Encoding/Decoding Audio Signals, Proc. ICMC, Glasgow, 175-177, 1990
  44. F. Boyer and R. Kronland-Martinet, Granular Resynthesis and Transformation of Sounds Through Wavelet Transform Analysis; Proc. ICMC, Ohio State U., 51-54, •1989
  45. R.N. Bracewell, The Fourier Transform and its Applications, New York: McGraw-Hill, 1986
  46. A.S. Bregman, Auditory Scene Analysis: The Perceptual Organization of Sound, London: MIT Press, 1989
  47. A.S. Bregman, Psychological data and computational ASA, in H. Okuno and D.F. Rosenthal, eds., Readings in Computational Auditory Scene Analysis, Hillsdale: Erlbaurn, 1996
  48. A.S. Bregman, Introduction to Auditory Scene Analysis, <http:// www. psych.mcgill.callabslauditory/introASA.html>, April 1996
  49. G.J. Brown, Computational Auditory Scene Analysis: A Representational Approach, PhD thesis CS-92-22, Dept. ofComp. Sci., Univ. of Sheffield, 1992
  50. G.J. Brown and M.P. Cooke, Computational Auditory Scene Analysis: Grouping sound sources using common pitch contours, Proc. lnst. Acoustics, Windermere, 439-446, November 1992
  51. G.J. Brown and M.P. Cooke, Perceptual Grouping of Musical Sounds, JNMR, 23 (2), 107-_132, 1994
  52. G.J. Brown and M.P. Cooke, Computational Auditory Scene Analysis, Computer Speech and Language, 8, 297-336, 1994
  53. G.J. Brown and M.P. Cooke, A neural oscillator model of primitive auditory grouping, Proc. IEEE Sig. Proc. Society Workshop on Applications of Sig. Proc. to Audio and Acoustics, New York, 53-56, October 1995
  54. G.J. Brown and M.P. Cooke, Temporal synchronisation in a neural oscillator model of primitive auditory stream segregation, IJCAI Workshop on Computational Auditory Scene Analysis, Montreal, August 1995
  55. G.J. Brown, M.P. Cooke, and E. Mousset, Are neural oscillations the substrate of auditory grouping?, ESCA Tutorial and Workshop on the Auditory Basis of Speech Perception, Keele, July 1996, and <ftp ://ftp. des. shef ac. uk/ share/spandh/pubslbrownlbcm-keele96. ps.Z> BrownJ 87
  56. Chan Chapman ChenM J.C. Brown and M.S. Puckette, Musical Information from a Narrowed Autoco"elation Function, Proc. ICMC, Urbana, 84-88, 1987
  57. J.C. Brown and M.S. Puckette, Calculation of a "na"owed" autoco"elationfunction, JASA., 85(4), 1595-1601, 1989
  58. J.C. Brown, Calculation of a Constant Q Spectral Transform, JASA., 89, 425- 434, 1991
  59. J.C. Brown and B. Zhang, Musical pitch tracking using the methods of conventional and "narrowed" autoco"elation, JASA., 89(5), 2346-2354, 1991, and <http:/ I sound. media. mit. edu/-brown/try/try. html> J.C. Brown, Musical fundamental frequency tracking using a pattern recognition method, JASA., 92(3), 1394-1402, 1992
  60. W. Biirck, P. Kotowski, and H. Lichte, Die Horbarkeit von Laufzeitdif.ferenzen, E1ektrotechn. Nachr.-Techn., 12, 355, 1935 [in German] E.M. Burns and W.D. Ward, Intervals, scales, and tuning, in The Psychology of Music, D. Deutsch, ed., 241-269, New York: Academic Press, 1982 D. Butler, A further study of melodic channeling, Perception and Psychophysics, 25, 264-268, 1979
  61. A. Calway, The Multiresolution Fourier Transform: A general Purpose Tool for Image Analysis, PhD thesis, Dept. of Comp. Sci., Univ. of Warwick, UK, September 1989
  62. N. Carver and V. Lesser, Blackboard systems for knowledge-based signal understanding, in A. Oppenheim and S. Nawab, eds., Symbolic and Knowledge-based Signal Processing, London: Prentice Hall, 1992
  63. C. Chafe, B. Mont-Reynaud, and L. Rush, Toward an intelligent editor of digital audio: Recognition of musical constructs, CMJ, 6(1), 30-41, Spring 1982 C. Chafe, D. Jaffe, K. Kashima, B. Mont-Reynaud, and J. Smith, Techniques for Note Identification• in Polyphonic Music, Proc. ICMC, Vancouver, 399-405, 1985, and Stanford Music Dept. Technical Report STAN-M-29
  64. C. Chafe and D. Jaffe, Source Separation and Note Identification in Polyphonic Music, Proc. ICASSP, vol. 2, 1289-1292, Tokyo, 1986
  65. C. Chafe, Simulating Performance on a Bowed Instrument, in Current Directions in Computer Music Research, M.V. Mathews and J.R. Pierce, eds., 185-198, Cambridge, Massachusetts: MIT Press, 1991
  66. D.C.B. Chan, P.J.W. Rayner, and S.J. Godsill, Multi-channel Blind Source Separation By Deco"elation, IEEE WASP AA, Mohonk, 1995
  67. D. Chapman, M. Clarke, M. Smith, and P. Archbold, Self-Similar Grain Distribution: A Fractal Approach to Granular Synthesis, Proc. ICMC, Hong Kong,212-213, 1996
  68. M. Chen <mchen@cse.psu.edu>, Gus Tester, <ftp://ftp.cdrorn.com/pub/ gus/utili dos/ gust est. zip>, April 1993
  69. S.S. Chen, Basis Pursuit, Proc. 28th Asilomar Conference on Signals, Systems, and Computers, vol. 1, 41-44, 1994, and <http:// playfair. stanford. edu/reports/chen _sf asilomar. ps.Z>
  70. S.S. Chen, Basis Pursuit, PhD thesis, Dept. of Statistics, Stanford Univ., November 1995, and <http:/ /playfair. stanford. edu/reports/chen _ s/thesis. ps.Z> S.S. Chen, D.L. Donoho, and M.A. Saunders, Atomic Decomposition by Basis Pursuit, <http://playfair. stanford. edu/reports/chen _ s/BasisPursuit. ps.Z> and Stanford Statistics Dept. Technical Report, 1996
  71. D.G. Childers and C.K. Lee, Co-Channel Speech Separation, Proc. ICASSP, 6.4.1-6.4.4, 1987
  72. A. Choi, A Least-Square Algorithm for Fundamental Frequency Estimation, Proc. ICMC, Banff, 284-287, 1995
  73. J.M. Chowning, The Synthesis of Complex Audio Spectra by Means of Frequency Modulation, J. AES, 21(7), 526-534, 1973 (reprinted in CMJ, 1(2), 46-54, Summer 1977)
  74. J.M. Chowning, L. Rush, B. Mont-Reynaud, C. Chafe, A. Schloss, and J.O. Smith, Intelligent Systems for the Analysis of Digitized Acoustic Signals, Final Report, Stanford Music Dept. Technical Report ST AN-M-15, 1984
  75. J.M. Chowning and D. Bristow, FM Theory and Applications, Yamaha Music Foundation, Tokyo, 1986
  76. • J.M. Chowning and B. Mont-Reynaud, Intelligent Analysis of Composite Acoustic Signals, Stanford Music Dept. Technical Report ST AN-M-36, 1986
  77. J.M. Chowning, Computer Music: A Grand Adventure and Some Thoughts About Loudness, Proc. ICMC, Tokyo, 2-8, 1993
  78. P.L. Chu, Quadrature Mirror Filter Design for an Arbitrary Number of Equal Bandwidth Channels, IEEE Trans. ASSP, 33(1), 203-218, February 1985
  79. P. Churchland, V.S. Ramachandran, and T. Sejnowski, A Critique of Pure Vision, in Large-Scale Theories of the Brain, C. Koch and J. Davis, eds., Cambridge, Massachusetts: MIT Press, 1994
  80. M.M. Cohen and D. Massaro, Synthesis of visible speech, Behaviour Research Methods, Instruments, and Computers, 22(2), 260-263, April1990
  81. R.R. Coifman, Y. Meyer, S. Quake, and M.V. Wickerhauser, Signal Processing and Compression with wave packets, Technical Report, Yale University, Maths Qept., April1990
  82. R.R. Coifman and M.V. Wickerhauser, Entropy-Based Algorithms for Best Basis Selection, Proc. Int. Conf on Wavelets and Applications, Toulouse, 1992 P.R. Cook, Implementation of Single Reed Instruments with Arbitrary Bore Shapes Using Digital Waveguide Filters, Stanford Music Dept. Technical Report STAN-M-50, 1988
  83. P.R. Cook, Identification and Control of Parameters in an Articulatory Vocal Tract Model, With Applications to the Synthesis of Singing, PhD thesis, Dept. ofElec. Eng., Stanford Univ., 1991
  84. P.R. Cook, D. Morrill, and J.O. Smith, An Automatic Pitch Detection and MIDI Control System for Brass Instruments, ASA conference, New Orleans, Dannenberg 92 Dannenberg 93a Dannenberg 93b
  85. Darton Daubechies 88a
  86. R.B. Dannenberg and C.W. Mercer, Real-time Software Synthesis on Superscalar Architectures, Proc. ICMC, San Jose, 174-177, 1992
  87. R.B. Dannenberg, Music representation issues, _techniques, and systems, CMJ, 17(3), 20-30, Fall 1993
  88. R.B. Dannenberg, The Implementation of Nyquist, A Sound Synthesis Language, Proc. ICMC, Tokyo, 168-171, 1993
  89. L. Darton, <darton@macon.wb.slb.com>, "how to detect COMPRESSED drive?" <news:comp.sys.ibm.programmer and private email>, 31 October 1995 I. Daubechies, Orthonormal bases of compactly supported wavelets, Communications in Pure and Applied Mathematics, 41, 1988
  90. I. Daubechies, Time-frequency localization operators: a geometric phase space approach, IEEE Trans. on Information Theory, 34, 605-612, 1988
  91. I. Daubechies, The Wavelet Transform, time-frequency localization and signal analysis, IEEE Trans. Information Theory, 36, 961-1005, 1990
  92. I. Daubechies, Ten Lectures on Wavelets, SIAM, Philadelphia, 1992
  93. G. Davidson, L. Fielder, and M. Antill, High Quality Transform Coding at 128 kbitsls, Proc. ICASSP, Albuquerque, 1990
  94. A. de Cheveigne, Separation of concu"ent harmonic sounds: Fundamental frequency estimation and a time-domain cancellation model of auditory processing, JASA, 93, 3271-3290, 1993
  95. A. de Cheveigne, S. McAdams, J. Laroche, and M. Rosenborg, Identification of concu"ent harmonic and inharmonic vowels: A test of the theory of harmonic cancellation and enhancement, JASA, 97, 3736-3749, 1995 A. de Cheveigne, A neural cancellation model ofF rrguided sound separation, Proc. ESCA workshop on the Auditory Basis of Speech Perception, Keele, 1996, and <ftp:/ /ftp.linguist.jussieu.fr/people/alain/papers/keele. ps.Z> B. Degazio, Towards a Chaotic Musical Instrument, Proc. ICMC, Tokyo, 393-395, 1993
  96. N. Delprat, Ph. Guillemain, and R. Kronland-Martinet, Parameters estimation for non-linear resynthesis methods with the help of a time- frequency analysis of natural sounds, Proc. ICMC, Glasgow, 88-90, 1990
  97. N. Delprat, B. Escudie, Ph. Guillemain, R. Kronland-Martinet, P. Tchamitchian, and B. Torresani, Asymptotic Wavelet and Gabor Analysis: Extraction of Instantaneous Frequencies, IEEE Trans. Information Theory, 38(2), 644-664, March 1992
  98. Ph. Depalle and X. Rodet, Synthese additive par FFT inverse, Rapport Interne IRCAM, Paris, 1990
  99. Ph. Depalle, G. Garcia, and X. Rodet, Analysis of Sound for Additive Synthesis: Tracking of Partials Using Hidden Markov Models, Proc. ICMC, Tokyo, 94-97, 1993 ph. Depalle, G. Garcia, and X. Rodet, Tracking of partials for additive sound synthesis using hidden Markov models, Proc. ICASSP, Minneapolis, April1993
  100. De Poli 83
  101. De Poli 93
  102. Elliott G. De Poli, A Tutorial on Digital Sound Synthesis Techniques, CMJ, 7(4), Winter 1983, also in The Music Machine-Selected Readings from Computer Music Journal, C. Roads, ed., Cambridge, Massachusetts: MIT Press, 1989
  103. G. De Poli and P. To nella, Self-organizing Neural Network and Grey's Timbre Space, Proc. ICMC, Tokyo, 260-263, 1993
  104. P. Desain, Graphical Programming in Computer Music, a Proposal, Proc. ICMC, The Hague, 161-166, 1986
  105. P. Desain and T. Brus, What Ever Happened To Our Beautiful Schematics, Proc. ICMC, Tokyo, 366-368, 1993
  106. P. Desain and H. Honing, Foot-tapping: a brief introduction to beat induction, Proc. ICMC, Aarhus, 78-79, 1994
  107. R. de Tintis, Grains: a software for real-time granular synthesis and sampling running on the IRIS-MARS workstation, Proc. XI Colloquium on Musical Informatics, Bologna, 221-224, 1995
  108. D. Deutsch, Grouping Mechanisms in Music, in The Psychology of Music, D. Deutsch, ed., 99-130, New York: Academic Press, 1982
  109. G. Di Giugno, A 256 Digital Oscillator Bank, Proc: ICMC, Massachusetts, 1976 A. Di Scipio, Real-time Polyphonic Time-shifting of Sound with Interactive Systems, Proc. XI Colloquium on Musical Informatics, Bologna, 19-22, 1995
  110. S. Dixon, A Dynamic Modelling Approach to Music Recognition, Proc. ICMC, Hong Kong, 83-86, 1996
  111. M. Dolson, The phase vocoder: a tutorial, CMJ, 10(4), 14-27, Winter 1986
  112. M. Dolson, Fourier-Transform-Based Timbral Manipulations, in Current Directions in Computer Music Research, M.V. Mathews and J.R. Pierce, eds., 185-198, Cambridge, Massachusetts: MIT Press, 1991
  113. L. Dorfman and D. Young, Atari ST: Introduction to MIDI Programming, New York: Bantam • B. Doval and X.O. Rodet, Fundamental Frequency Estimation using a New Harmonic Matching Method, Proc. ICMC, Montreal, 555-558, 1991
  114. W.J. Dowling, Melodic Information Processing and its Development, in The Psychology of Music, D. Deutsch, ed., 413-429, New York: Academic Press, 1982 S. Dubnov, N. Tishby, and D. Cohen, Hearing Beyond the Spectrum, JNMR., 24, 342-368, 1995
  115. J. Duessenberry, Understanding Amplitude Modulation, Electronic Musician, November 1990
  116. B. Eaglestone and S. Oates, Analytical Tools for Group Additive Synthesis, Proc. ICMC, Glasgow, 66-68, 1990
  117. T. Edwards, Discrete Wavelet Transforms: Theory and Implementation, <ftp:// isl.stanford.edu/ pub/ godfrey/ reports/ wavelets/ tim_edwards/ wave _paper/ wave_paper.ps>, 9 July 1992
  118. D.F. Elliott and K.R. Rao, Fast transforms -algorithms, analyses, applications, Academic Press, Orlando, Florida, 1982
  119. Ellis 91 Ellis 92a
  120. Feldman Femandez-Cid D.P.W. Ellis and B.L. Vercoe,A wavelet-based sinusoid model of sound for auditory signal separation, Proc. ICMC, Montreal, 86-89, 1991
  121. D.P.W. Ellis and B.L. Vercoe, A Perceptual Representation of Sound for Auditory Signal Separation, Proc. 123rd meeting of ASA, Salt Lake City, May 1992, and <ftp://sound.media.mit.edu/pub/Papers/dpwe-asa92slc.ps.gz> D.P.W. Ellis, Timescale modifications and wavelet representations, Proc. ICMC, San Jose, 6-9, 1992
  122. D.P.W. Ellis, A Perceptual Representation of Audio, MSc dissertation, Dept. ofElec. Engg. and Comp. Sci., MIT, 1992
  123. D.P.W. Ellis, Hierarchical models of hearing for sound separation and reconstruction, Proc. IEEE WASP AA, Mohonk, October 1993, and MIT Media Lab Perceptual Computing Group Tech. Report #219
  124. D.P.W. Ellis, A Computer Implementation of Psychoacoustic Grouping Rules, Proc. 12th Int. Conf on Pattern Recognition, Jerusalem, October 1994, and MIT Media Lab Perceptual Computing Group Tech. Report #224 (rev. 2), and <ftp ://sound. media. mit. edu/pub/Papers/ dpwe-ICPR94. ps. gz> D.P.W. Ellis and D. Rosenthal, Mid-level representations for Computational Auditory Scene Analysis, Proc. Computational Auditory Scene Analysis Workshop, Int. Joint Conf on Artificial Intelligence, Montreal, August 1995
  125. D.P.W. Ellis, Underconstrained stochastic representations for top-dawn computational auditory scene analysis, IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, 1995
  126. D.P.W. EUis, Prediction-driven Computational Auditory Scene Analysis, PhD thesis, Dept. ofElec. Eng. and Comp. Sci., MIT, 1996
  127. D.P.W. Ellis, Prediction-driven Computational Auditory Scene Analysis for Dense Sound Mixtures, to be presented at the ESCA workshop on the Auditory Basis of Speech Perception, Keele, UK, July 1996
  128. D.P.W. Ellis, Hard Problems in Computational Auditory Scene Analysis for Dense Sound Mixtures, <http://sound.media.mit.edu/-dpwe/writinglhard- probs.htrnl>, 1996
  129. • Emagic Inc., Logic Audio (sales literature), <http://www.emagic.de/english/ products/windows/LOGICAUDIO.htrnl>, 1997
  130. T. Erbe, SoundHack User's Manual, Mills College, Oakland, May 1992
  131. R. Ethington ancJ B. Punch, SeaWave: A System for Musical Timbre Description, CMJ, 18(1), 30-39, Spring 1994
  132. B. Feiten and T. Ungvary, Sound data base using spectral analysis reduction and an additive synthesis model, Proc. ICMC, Glasgow, 72-74, 1990
  133. B. Feiten, R. Frank, and T. Ungvary, Organizations of sounds with neural nets, Proc. ICMC, Montreal, 441-444, 1991
  134. M. Feldman <myndale@cairo.anu.edu.au>, PC Games Programmer's Encyclopaedia, <http://www.qzx.com/pc-gpe/>, May 1994
  135. P. Femandez-Cid and F.J. Casajtis-Quiros, DSP based reliable pitch-to- MIDI converter by harmonic matching, Proc. ICMC, Aarhus, 307-310, 1994
  136. Goodwin Goto 94 Goto 96 Gozum Graps K. Fitz, W. Walker, and L. Haken, Extending the McAulay-Quatieri Analysis for Synthesis with a Limited Number of Oscillators, Proc. ICMC, Banff, 381-382, 1995 .
  137. H. Fletcher, E.D. Blackham, and R. Stratton, Quality of piano tones, JASA, 34, 749-761, 1962
  138. H. Fletcher, E.D. Blackham, and D.A. Christensen, Quality of organ tones, JASA, 35,314-325, 1963
  139. H. Fletcher, Normal Vibration Frequencies of a Stiff Piano String, in Musical Acoustics -Piano and Wind Instruments, E.L. Kent, ed., Stroudsburg: Dowden, Hutchinson and Ross, 1977
  140. H. Fletcher, Quality of Piano Tones, in Musical Acoustics -Piano and Wind Instruments, E.L. Kent, ed., Stroudsburg: Dowden, Hutchinson and Ross, 1977 F. Fontana and D. Rochesso, A New Formulation of the 2D-Waveguide Mesh for Percussion Instruments, Proc. XI Colloquium on Musical Informatics, Bologna, 27-30, 1995
  141. S. Foster, W.A. Schloss, and A.J. Rockmore, Toward an Intelligent Editor of Digital Audio: Siglial Processing Methods, CMJ, 6(1), 42-51, Spring 1982
  142. J. Frandsen <jesperf@daimi.aau.dk>, PCXDUMP, <http:// www. daimi.aau.dk/-jesperf7Pcxdump/pcxdump.html>, February 1994
  143. A. Freed, X. Rodet, and Ph. DepaUe, Synthesis and Control of Hundreds of Sinusoidal Partials on a Desktop Computer without Custom Hardware, Proc. ICMC, Tokyo, 98-101, 1993
  144. A. Freed, X. Rodet, and Ph. DepaUe, Synthesis and Control of Hundreds of Sinusoidal Partials on a Desktop Computer without Custom Hardware, Proc. ICSPAT, 1993
  145. D. Gabor, Theory of communication, J. Inst. Elec. Engg., 93(1II), 429-457, 1946 D. Gabor, Acoustical quanta and the theory of hearing, Nature, 159(4044), 1947
  146. J.L. Goldstein, Auditory Nonlinearity, JASA, 41, 676-689, 1967
  147. J.L. Goldstein, An optimum processor for the central formation of pitch of complex tones, JASA, 54(6), 1496-1516, 1973
  148. M. Goodwin and A. Kogon, Overlap-add synthesis of nonstationary sinusoids, Proc. ICMC, Banff, 355-356, 1995
  149. M. Goto and Y. Muraoka, A Sound Source Separation System for Percussion Instruments, Trans. Institute of Electronics, Information and Communication Engineers, 177-D-11(5), 901-911, May 94 [in Japanese]
  150. M. Goto, I. Hidaka, H. Matsumoto, Y. Kuroda, andY. Muraoka, A Jazz Session System for Interplay among All Players, Proc. ICMC, Hong Kong, 346-349, 1996
  151. L. Gozum and M. Gozum, VGACAP and VGAFIL (documentation), <ftp:// sunsite.doc.ic.ac. uk/packageslsimtelnet/msdoslvgalvgacap81.zip>, 1991
  152. A. Graps, An Introduction to Wavelets, IEEE Computational Science and Engineering, 2(2), 50-61, Summer 1995 D.M. Green, Temporal Auditory Acuity, Psychological Review, 78(6), 540- 551, 1971
  153. G.D. Green, <greeng@crl.com>, "Contrabass saxes", private email, July 1996, and CONTRABASS-L mailing list, 1 ( 45), <http://www.contrabass.com/ pages!listhtml>, 4 October 1996
  154. J.M. Grey, An Exploration of Musical Timbre: using Computer-based Techniques for Analysis, Synthesis and Perceptual Scaling, PhD thesis, Stanford Univ., and Stanford Music Dept Technical Report STAN-M-2, 1975
  155. J.M. Grey, Multidimensional Perceptual Scaling of Musical Timbres, JASA, 61(5), 1270-1277, May 1977
  156. J.M. Grey and J.A. Moorer, Perceptual Evaluation of Synthesized Musical Instrument Tones, JASA, 62(2), 454-462, August 1977
  157. R. Gribonval, Ph. Depalle, X. Rodet, E. Bacry, •and S. Mallat, Sound Signals Decomposition Using a High Resolution Matching Pursuit, Proc. ICMC, Hong Kong, 293-296, 1996
  158. E. Grieg, Piano Concerto in A minor, Op. 16, 1868 (audio: Compact Disc CLS4013, Dusseldorf: Mediaphon) (score: Leipzig: C.F. Peters)
  159. E. Grieg, Death of Aase, Peer Gynt Suite No. 1, Op. 46, 1876 (audio: Compact Disc CLS40l3, DUsseldorf: Mediaphon) (score: London: Eulenburg)
  160. L. Grubb and R.B. Dannenberg, Automating Ensemble Performance, Proc. ICMC, Aarhus, 63-69, 1994
  161. Ph. Guillemain and R. Kronland-Martinet, Additive resynthesis of sounds using continuous time-frequency representations, Proc. ICMC, San Jose, 10- 13, 1992
  162. L. Haken, Real-Time Fourier Synthesis of Ensembles with Timbral Interpolation, PhD thesis, Dept of Electrical and Computer Engineering, Univ. of Illinois, 1989
  163. L. Haken, Computational Methods for Real-Time Fourier Synthesis, IEEE Trans. ASSP, 40(9), 2327-2329, 1992
  164. G. Hall, The Dimensions of Delay, Electronic Musician, September 1990
  165. S. Handel, Listening, Cambridge, Massachusetts: MIT Press, 1989
  166. B.A. Hanson and D.Y. Wong, The Harmonic Magnitude Suppression Technique for Intelligibility Enhancement in the Presence of Interfering Speech, Proc. ICASSP, 18A.5.1-18A.5.4, 1984
  167. F. J. Harris, On the use of windows for harmonic analysis with the DFT, Proc. IEEE 66, 51-83, 1978
  168. M. Hawley, Structure out of Sound, PhD thesis, MIT Media Lab., 1993
  169. W. Heinbach, Geh6rgerecht Repriisentation von Audiosignalen durch das Teiltonzeitmuster, PhD thesis, Technical University ofMunich, 1987
  170. Homer93 Homer 95a
  171. Homer96 W. Heinbach, Aurally adequate signal representation: The part-time-tone- pattern, Acustica, 67, I 13-121, 1988
  172. H.L.F. von Helmholtz, On the Sensations of Tome as a Physiological Basis for the Theory of Music, 1863, 4th ed., trans. A.J." Ellis, New York: Dover, 1954 M. Helmuth, Granular Synthesis with Cmix and MAX, Proc. ICMC, Tokyo, 449-452, 1993
  173. M. Helmuth, Multidimensional Representation of Electroacoustic Music, JNMR, 25, 77-103, 1996
  174. D.J. Hermes, Measurement of pitch by harmonic summation, JASA, 83(1), 257-264, 1988
  175. W. Hesseler <hesseler@athene.informatik.uni-bonn.de>, QuickView, <ft.p:/1 ft.p.rhrz.uni-bonn.de/pub/institutelhesseler/qv I 03a.zip>, 1995
  176. I. Hidaka, M. Goto, andY. Muraoka, An Automatic Jazz Accompaniment System Reacting to Solo, Proc. ICMC, Banff, 167-170, 1995
  177. I.J. Hirsh, Auditory Perception of Order, JASA, 39, 759, 1959
  178. Hohner Midia, Siiundscore (program), <http://www.hohnermidia.com/ basic.html>, September 1996
  179. R.R. HOidrich, Zur Analyse und Resynthese von Klangsignale!J unter Verwendung von Zeit-Frequenze-Repriisentationen mit Verbesserter Lokalisation der Signalenergie, PhD thesis, Technical Univ. Graz, Austria, 1994 [in German]
  180. R.R. Holdrich, The Improved Spectrogram -How to Obtain an Accurate Signal Representation for Sound Resynthesis?, Proc. Int. Simposio Brasiliera de Computacao e Musica, 1994
  181. R.R. Holdrich, Frequency Analysis of Non-Stationary Signals Using a Time- Frequency Mapping of the DFT Magnitude, Proc. ICSPAT, Dallas, 1994
  182. R.R. Holdrich, An Accurate Signal Representation for Sound Resynthesis Utilizing a Time-Frequency Mapping of the DFT-Magnitude, Proc. ICMC, Banff, 592-594, 1995
  183. Y. Horiuchi, A. Fujii, and H. Tanaka, A Computer Accompaniment System Considering Independence of Accompanist, Japan Music and Computer Science Society, Proc. of Summer Symposium, 1992 [in Japanese]
  184. Y. Horiuchi and H. Tanaka, A Computer Accompaniment .System With Independence, Proc. ICMC, Tokyo, 418-420, 1993
  185. A. Homer, J. Beauchamp, and N. Packard, Timbre Breeding, Proc. ICMC, Tokyo,396-398, 1993
  186. A. Homer, Envelope Matching with Genetic Algorithms, JNMR, 24, 318-341, 1995 A. Homer, Wavetable Matching Sjmthesis of Dynamic Instruments with Genetic Algorithms, J. AES, 43( 11 ), 916-931, 1995
  187. A. Homer and J. Beauchamp, Piecewise-Linear Approximation of Additive Synthesis Envelopes: A Comparison of Various Methods, CMJ, 20(2), 72-95, Summer 1996
  188. Horry
  189. Jaffe Jain Jimosy Y. Horry, A Graphical User Interface for MIDI Signal Generation and Sound Synthesis, Proc. ICMC, Aarhus, 276-279, 1994
  190. A.D. Houghton, A.J. Fisher, and T.F. Malet, An ASIC for Digital Additive Sine-wave Synthesis, CMJ, 19(3), 26-31, Fall1995 Y.H. Hu, CORDIC-Based VLSI Architectures for Digital Sound Processi~g. IEEE Signal Processing Magazine, July 1992 RD. Hughes and M.L. Heron, Approximate Fourier transform using square waves, Proc. lEE, 136(A4), 223-227, July 1989
  191. R Hung, N.H.C. Yung, and P.Y.S. Cheung, The Analysis and Resynthesis of Sustained Musical Signals in the Time Domain, Proc. ICMC, Hong Kong, 206-209, 1996
  192. K.R Hyun, R Banerjea, M. Kim, H. Latchman, and S.I. Sudharsanan, A Real-Time Implementation of MPEG Audio Layer I Decoding on a Fixed- Point DSP Platform, Proc. ICMC, Tokyo, 412-414, 1993
  193. W. Inoue, S. Hashimoto, and S. Ohteru, A Computer Music System for Human Singing, Proc .. JCMC, Tokyo, 150-153, 1993 _ W. Inoue, S. Hashimoto, and S. Ohteru, Adaptive Karaoke System-Human Singing Accompaniment Based on Speech Recognition, Proc. ICMC, Aarhus, 70-77, 1994
  194. ISO/IECJTC1/SC2/WG11 MPEG 91 Committee Draft, Coding of moving pictures and Associated audio for digital storage media at up to 1.5 Mbitls, November 1991
  195. T. Itagaki, A. Purvis, and P.D. Manning, Real-time Synthesis on a Multi- processor network, Proc. ICMC, Aarhus, 382-385, 1994
  196. T. ltagaki, D.K. Phillips, P.D. Manning, and A. Purvis, An Implementation of Optimised Methods for Real-time Sound Synthesis on a Multi-processor Network, Book of Abstracts on Parallel Computing, Gent, 1995
  197. T. ltagaki, D.J.E. Nunn, D.K. PhiUips, D. Batjakis, A. Purvis, and P.D. Manning, Activity Report, XI Colloquium on Musical Informatics, Bologna, 51-54, 1995
  198. T. Itagaki, P.D. Manning, and A. Purvis, Real-time Granular Synthesis on a Distributed Multi-processor Platform, Proc. ICMC, Hong Kong, 287-288, 1996 T. ltagak.i, S. Johnson, P.D. Manning, D.J.E. Nunn, D.K. PhiUips, A. Purvis, and J. Spanier, Durham Music Technology: Activity Report, Proc. ICMC, Hong Kong, 126-128, 1996
  199. D.A. Jaffe, Ten Criteria for Evaluating Synthesis Techniques, CMJ, 19(1), 76-87, Spring 1995
  200. A.K. Jain, A fast Karhunen-Loeve transform for a class of random processes, IEEE Trans. Commun., COM-24, 1023-1029, 1976
  201. z. Janosy, M. Karjalainen, and V. Viilimiik.i, Intelligent Synthesis Control with Applications to a Physical Model of the Acoustic Guitar, Proc. ICMC, Aarhus, 402-406, 1994
  202. C. Jansen, Sine Circuitu-10,000 high quality sine waves without detours, Proc. ICMC, Montreal, 222-225, 1991
  203. C. Jansen, Sine Circuitu -Real-time analysis, manipulation and (re)synthesis, Proc. ICMC, San Jose, 451-452, 1992
  204. S.-B. Jaw and S.-C. Pei, Two-band IIR quadrature mirror filter design, Electronics Letters, 26(20), 1687-1689, September 1990
  205. B. Jawerth and W. Sweldens, An overview of wavelet based multireso/ution analysis, SIAM Rev., 36(3), 377-412, 1994, and <http://cm.bell-labs.com/ who/wim/papers/overview. ps.gz> D.L. Jones and T.W. Parks, On computing equally spaced samples of a complex Gaussian function, IEEE Trans. ASSP, 35(10), 1987
  206. D.L. Jones and T.W. Parks, Generation and combination of grains for music synthesis, CMJ, 12(2), 27-34, Summer 1988
  207. Jordan Hargraphics Software Inc., SVGABGI, <gopher:// micros. hensa. ac. uk: 70111/rnicros/ibmpc/ dos/ gl g770/>, January 1995
  208. J.H. Justice, Analytic Signal Processing in Music Computation, IEEE Trans. ASSP, 27(6), 670-684; 1979
  209. S. Kadambe and G.F. Boudreaux-Bartels, Application of the Wavelet Transform for Pitch Detection of Speech Signals, IEEE Trans. Information Theory, 38(2), 917-924, 1992
  210. J.F. Kaiser, Nonrecursive digital filter design using the IO-sinh window function, Proc. IEEE Int. Symposium on Circuits and Systems, 20-23, San Francisco, 1974
  211. J.F. Kaiser, On the fast generation of equally spaced values of the Gaussian function A exp(-at*t), IEEE Trans. ASSP, 35(10), 1987
  212. M. Karjalainen, V. Valimaki, and Z. Janosy, Towards High-Quality Sound Synthesis of the Guitar and String Instruments, Proc. ICMC, Tokyo, 56-63, 1993
  213. M. Karjalainen and J. Smith, Body Modeling Techniques for String Instrument Synthesis, Proc. ICMC, Hong Kong, 232-239, 1996
  214. K. Kashino and H. Tanaka, A sound source separation system using spectral features integrated by [the] Dempster's law of combination, In Annual Report of the Engineering. Research Institute, Faculty of Engineering, University of Tokyo,67-72, 1992
  215. K. Kashino and H. Tanaka, A Sound Source Separation System with the Ability of Automatic Tone Modelling, Proc. ICMC, Tokyo, 24&-255, 1993
  216. K. Kashino, K. Nakadai, T. Kinoshita, and H. Tanaka, Organization of Hierarchical Perceptual Sounds: Music Scene Analysis with Autonomous Processing Modules and a Quantitative Information Integration Mechanism, Proc. Int. Joint Con£ on Artificial Intelligence, Workshop on Computational Auditory Scene Analysis, Montreal, August 1995, and <http://www. mtl. t.u-tokyo.ac.jp/Researchlpaper/1995/E95-conference-kashino-1. ps.gz> K. Kashino, K. Nakadai, T. Kinoshita, and H. Tanaka, Application of Bayesian Probability Network to Music Scene Analysis, Proc. Int. Joint Con£ on Artificial Intelligence, Workshop on Computational Auditory Scene Analysis, Montreal, August 1995, and <http://www.mtl.t.u-tokyo.ac.jp/ Research/paper/ 199 5/E95-conference-kashino-2. ps. gz> Katayose 88
  217. H. Katayose, M. Imai, and S. Inokuchi, Sentiment Extraction in Music, Proc. 9th Int. Conf. on Pattern Recognition, 1083-1087, Washington, 1988
  218. H. Katayose and S. Inokuchi, The Kansei Music System, CMJ, 13(4), 72-77, Winter 1989
  219. Katayose 90a H. Katayose and S. Inokuchi, The Kansei Music System '90, Proc. ICMC, Glasgow, 308-310, 1990
  220. Katayose 90b
  221. H. Katayose, T. Fukuoka, K. Takami, and S. Inokuchi, Expression extraction in virtuoso music performances, Proc. 1Oth Int. Conf on Pattern Recognition, New Jersey, 780-784, June 1990
  222. H. Katayose, T. Kanamori, K. Kamei, Y. Nagashima, K. Sato, S. lnokuchi, and S. Simura, Virtual Performer, Proc. ICMC, Tokyo, 138-145, 1993
  223. Kendall91 G.S Kendall, W.L. Martens, and S.L. Decker, Spatial Reverberation: Discussion and Demonstration, in Current Directions in Computer Music Research, M.V. Mathews and JR. Pierce, eds., 65-87, Cambridge, Massachusetts: MIT Press, 1991
  224. G.S. Kendall, A 3-D Sound Primer: Directional Hearing and Stereo Reproduction, CMJ, 19(4), 23-46, Winter 1995
  225. Kernighan B.W. Kernighan and D.M. Ritchie, The C Programming Language, London: Prentice-Hall, 1988
  226. Kiang N.Y.S. Kiang and E.C. Moxon, Tails of tuning curves of auditory-nerve fibers, JASA, 55(3), 620-630, 1974
  227. Kleczkowski P. Kleczkowski, Group Additive Synthesis, CMJ, 13(1), 12-20, Spring 1989
  228. K. Kodera, Analyse numerique de signeaux geophysiques nonstationaires, PhD thesis, Univ. ofParis, 1976 [in French] Kodera 78 K. Kodera, R. Gendrin, and C. Villedary, Analysis of Time-Varying Signals with Small BT Values, IEEE Trans. ASSP, 26(1), 64-76, 1978
  229. Kohonen T. Kohoilen, The Self-Organizing Map, Proc. IEEE, 78(9), 1464-1480, 1990
  230. Kottick E.L. Kottick, K.D. Marshall, and T.J. Hendrickson, The Acoustics of the Harpsichord, Scientific American, 94-99, February 1991
  231. Kriese C. Kriese and S. Tipei, A compositional approach to additive synthesis on supercomputers, Proc. ICMC, San Jose, 394-395, 1992
  232. Kronland-Martinet 87 R. Kronland-Martinet, J. Morlet, and A. Grossmann, Analysis of sound patterns through wavelet transforms, Int. J. of Pattern Recognition and Artificial Intelligence, 2, 97-126, 1987
  233. • Kronland-Martinet 88 R. Kronland-Martinet, The Wavelet Transform for Analysis, Synthesis, and Processing of Speech and Music Sounds, CMJ, 12(4), ll-20, Winter 1988
  234. Kronland-Martinet 93 R. Kronland-Martinet and Ph. Guillemain, Towards non-linear resynthesis of instrumental sounds, Proc. ICMC, Tokyo, 86-93, 1993
  235. Kuhn W.B. Kuhn, A Real-Time Pitch Recognition Algorithm for Music Applications, CMJ, 14(3), 60-71, Fall1990
  236. Kurz M. Kurz and B. Feiten, Physical modelling of a stiff string by numeric integration, Proc. ICMC, Hong Kong, 361-364, 1996
  237. Majernik C. Kussmaul, Applications of the Wavelet Transform at the Level of Pitch Contour, Proc. ICMC, Glasgow, 483-486, 1990
  238. B. Laden and D.H. Keefe, The Representation of Pitch in a Neural Net Model of Chord Classification, in Music and Connectionism, P.M. Todd and D.G. Loy, eds., Cambridge, Massachusetts: MIT Press, 1991
  239. B. Laden, A Parallel Learning Model of Musical Pitch Perception, JNMR, 23, 133-144, June 1994
  240. J.E. Lane, Pitch Detection Using a Tunable IIR Filter, CMJ, 14(3), 46-59, Fal11990 M. Leman, Schema-Based Tone Center Recognition of Musical Signals, JNMR, 23(2), 169-204, 1994
  241. J.C.R. Licklider, Three Auditory Theories, in Psychology: A Study in Science, Vol. 1, S. Koch, ed., New York: McGraw-Hill, 1959
  242. E. Lindemann, M. Starkier, and F. Dechelle, The IRCAM Musical Workstation: Hardware Overview and Signal Processing Features, Proc. ICMC, Glasgow, 132-135, 1990
  243. E. Lindemann, F. Dechelle, B. Smith, and M. Starkier, The Architecture of The IRCAMMusical Workstation, CMJ, 15(3), 41-49, 1991
  244. C. Lippe and M. Puckette, Musical Performance using the IRCAM Workstation, Proc. ICMC, Montreal, 533-536, 1991
  245. C. Lippe, A Musical Application of Real-time Granular Sampling Using the IRCAM Signal Processing Workstation, Proc. ICMC, Tokyo, 190-193, 1993
  246. C. Lippe, M. Puckette, Z. Settel, V. Puig, and J-P. Jullien, The IRCAM. Signal Processing Workstation and IRCAM Max User Groups: Future Developments and Platforms, Proc. ICMC, Tokyo, 446-448, 1993
  247. D. Y-0. Lo, Techniques for Timbral Interpolation, Proc. ICMC, The Hague, 1986 F. Lopez-Lezcano, PadA1aster: banging on algorithms with alternative controllers, Proc. ICMC, Hong Kong, 425-427, 1996
  248. G. Loy, Composing with Computers -a Survey of Some Compositional Formalisms and Music Programming Languages, in Current Directions in Computer Music Research, M.V. Mathews and J.R. Pierce, eds., 291-396, Cambridge, Massachusetts: MIT Press, 1991
  249. LT Sound, Inc., The Thompson Vocal Eliminator, sales literature, 1996
  250. E. Maggi and F. Dechelle, The evolution of the graphic editing environment for the IRCAM musical workstation, Proc. ICMC, Hong Kong, 185-187, 1996
  251. R.C. Maher, An Approach for the Separation of Voices in Composite Musical Signals, PhD thesis, University oflllinois, Urbana, 1989
  252. R.C. Maher, Evaluation of a Method for Separating Digitized Duet Signals, J. AES, 38(12), 956-979, 1990
  253. V. Majernik and J. Kaluzny, On the Auditory Uncertainty Relations, Acustica, 43, 132, 1979
  254. Maksym Mallat 89
  255. N. Maksym, Real-time pitch extraction by adaptive prediction of the speech waveform, IEEE Trans. on Audio and Electroacoustics, 21, 149-154, 1973
  256. S.G. Mallat, A Theory for Multiresolution Signal Decomposition: The Wavelet Representation, IEEE Trans. on Patterri Analysis and Machine Intelligence, 11(7), 674-693, July 1989
  257. S.G. Mallat and Z. Zhang, Matching Pursuit with Time-Frequency Dictionaries, IEEE Trans. on Signal Processing, 41, 3397-3415, 1993
  258. P.D. Manning, Electronic and Computer Music, Oxford: Oxford University Press, 1985
  259. A. Mansour and C. Jutten, A Simple Cost Function For Instantaneous and Convolutive Sources Separation, Actes du XVeme Colloque GRETSI, 301- 304, September 1995
  260. J.D. Markel and A.B. Gray, Linear Prediction of Speech, Berlin: Springer- Verlag, 197 6
  261. S.L. Marple, Jr., Digital Spectral Analysis with Applications, Englewood Cliffs: Prentice-Hall, 1987
  262. D. Martin and D. Ward, Subjective evaluation of musical scale temperament in pianos, JASA, 33, 582-585, 1961
  263. K.D. Martin, Estimating azimuth and elevation from interaural differences, IEEE WASP AA,. Mohonk 1995, and <ftp://sound.media.mit.edu/pub!Papers/ kdm-mohonk95. ps.Z>
  264. K.D. Martin, Blackboard-Based Transcription Project, <http:// sound.media.mit. edu/-kdm/researchlbboard/>, April 1996
  265. D.K. Mason <76546.1321@compuserve.com>, 'Dave's Targa Animator' and 'Dave's Flic Viewer' programs, <http://www. povray.org/povcd/ programs/povutiVdfv _ dtaldta21 pb.zip>, 1995
  266. M. V. Mathews, The Technology of Computer Music, Cambridge, Massachusetts: MIT Press, 1969
  267. M.V. Mathews and J.R. Pierce, The Bohlen-Pierce Scale, in Current Directions in Computer Music Research, M.V. Mathews and J.R. Pierce, eds., 165-173, Cambridge, Massachusetts: MIT Press, 1991
  268. MathSoft Inc., MathCad, <http://www.mathsoft.com/all60.htm>, 1994
  269. P. Matthews and N. McWhirter, The Guinness Book of Records, London: Guinness Publishing, 1995
  270. S.J. McAdams, Spectral Fusion, Spectral Parsing and the Formation of Auditory Images, PhD thesis, Stanford Univ., 1984
  271. R.J. McAulay and T.E. Quatieri, Speech analysis/synthesis based on a sinusoidal representation, Trans. ASSP, ASSP-34, 744-754, 1986
  272. D.K. Mellinger, Event formation and separation in musical sound, PhD thesis, Stanford Univ., 1991
  273. D.K. Mellinger and B.M. Mont-Reynaud, SoundExplorer: A Workbench for Investigating Source Separation, Proc. ICMC, Montreal, 90-93, 1991
  274. F. Mendelssohn, Sonata III, from Sechs Sonaten.fiir die Orgel, Op. 65, 1844 (score: Leipzig: Breitkopf & Hartel, republished (1967) Farnborough, UK: Gregg Press, 196 7)
  275. S. Menninga, "WAV -> MIDI? ... about Physics and feasibility", private email, 7 November 1996
  276. Y. Meyer, Wavelets: Examples and Applications, SIAM, Philadelphia, 1993
  277. Microsoft Inc., QBasic (part of MS-DOS), 1993
  278. M. Milicevic, The Impact of Fractals, Chaos, and Complexity Theory on Computer Music Composition, Proc. ICMC, Hong Kong, 473-476, 1996
  279. J.R. Miller and E.C. Carterette, Perceptual space for musical structures, JASA, 58(3), 711-720, 1975
  280. F. Mintzer, Filters for Distortion-Free Two-Band Multirate Filter Banks, IEEE Trans. ASSP, 33(3), 1985
  281. E. Miranda, Cellular Automata Synthesis of Acoustic Particles, <http:!/ www.epcc.ed.ac.uk!tracs/eduardo.html>, 11 May 1995
  282. O.M.M. Mitchell, C.A. Ross, and G.B. Yates, Signal processing for a cocktail party effect, JASA, 50(2), 656,660, 1971
  283. B. Mont-Reynaud, Problem-solving Strategies in a Music Transcription System, Proc. Int. Joint Conf. on Artificial Intelligence, 916-918, 1985
  284. •-• - B. Mont-Reynaud, On Hearing Music Vis'ual/y, Proc. AAAI, Special Session on Artificial Intelligence and Music, St. Paul, 1988
  285. B. Mont-Reynaud and D. Mellinger, Source Separation by Frequency Co- Modulation, Proc. of the First Int. Conf. on Music Perception and Cognition, Kyoto, 99-102, 1989
  286. B. Mont-Reynaud and E. Gresset, PRISM: Pattern Recognition In Sound and Music, Proc. ICMC, Glasgow, 153-155, 1990
  287. B. Mont-Reynaud, SeeMusic: A Tool for Music Visualization, Proc. ICMC, Tokyo, 457-460, 1993
  288. B.C.J. Moore, B. Glasberg, and R. W. Peters, Relative dominance of individual partials in determining the pitch of complex tones, JASA, 77, 1853- 1860, 1985
  289. B.C.J. Moore, Hearing, San Diego: Academic Press, 1995
  290. D.R. Moore, Physiology of higher auditory system, British Journal of Audiology, 24, 131-137, 1987
  291. F.R. Moore, Table Lookup Noise for Sinusoidal Digital Oscillators, CMJ, 1(2), 26-29, Summer 1977
  292. F.R. Moore, The Dysfunctions of MIDI, CMJ, 12(1), 19-28, Spring 1988
  293. F.R. Moore, Elements of Computer Music, Englewood Cliffs, Prentice-Hall, 1990 J.A. Moorer, On the Segmentation and Analysis of Continuous Musical Sound by Digital Computer, PhD thesis, Dept. of Computer Science, Stanford Univ., and Stanford Music Dept. Technical Report ST AN-M-3, 1975
  294. • J.A. Moorer, The Synthesis of Complex Audio Spectra by Means of Discrete Summation Formulas, J. AES, 24(9), 717-727, November 1976
  295. J.A. Moorer, Signal Processing aspects of Computer Music: A Survey, Proc. IEEE, 65, 1108-1137, 1977
  296. J.A. Moorer, On the Transcription of Musical Sound by Computer. CMJ, 1(4), 32-38, Winter 1977
  297. J.A. Moorer, The use of the phase vocoder in computer music applications, J. AES, 24(9), 1978
  298. E.I. Moreno, The Existence of Unexplored Dimensions of Pitch: Expanded Chromas, Proc. ICMC, San Jose, 402-403, 1992
  299. Motorola Inc., DSP96002 IEEE Floating-Point Dual-Port Processor User's Manual, 1989
  300. T. Nakatani, H.G. Okuno, and T. Kawabata, Auditory stream segregation in auditory scene analysis with a multi-agent system, AAAI Conference Proceedings, 1994
  301. K. Naoi, S. Ohteru, and S. Hashimoto, Automatic Accompaniment Using Real Time Assigning Note Value, Convention Record of the Acoustical Society ofJapan, Spring 1989 [in Japanese]
  302. S.H. Nawab and E. Dorken, Efficient STFT approximation using a quantization and differencing method, Proc. ICASSP, Minneapolis, 587-590, April 1993 .. - J.A. Naylor and S.F. BoD, Techniques for suppression of an Interfering Talker in Co-channel Speech, Proc. ICASSP, 6.12.1-6.12.4, 1987
  303. S. Neely, <neely@boystown.org>, Cochlear Mechanics Tutorial, Boys Town National Research Hospital, <http :1 /www. boystown. org/cel/ cochmech. html>, March 1995
  304. D. Newland, Harmonic wavelet analysis, Proc. Royal Soc. London Series A- Mathematical and Physical Sciences, 443, 203-225, 1993
  305. D. Newland, Harmonic and Musical Wavelets, Proc. Royal Soc. London Series A-Mathematical and Physical Sciences, 444, 605-620, 1994
  306. R.C Nieberle, A Fast Communication Interface to the CAMP-DSP-Subsystem for general purpose Sound-Synthesis, Analysis and Processing, Proc. ICMC, Montreal, 529-532, 1991
  307. T. Niihara, H. Katayose, and S. Inokuchi, Transcription •of Sung Song, Proc. ICASSP, IEEE, New York, 1986
  308. A.M. NoD, Cepstrum Pitch Determination, JASA, 41, 293-309, 1966
  309. D.J.E. Nunn, The Acoustics of Brass Instruments, Sixth Year Studies Physics Project, George Watsons College, Edinburgh, 1984
  310. D.J.E. Nunn, A. Purvis, and P.D. Manning, Source Separation and Transcription of Polyphonic Music, Proc. International Colloquium on New Music Research, Gent, Belgium, 1994
  311. D.J.E. Nunn, A. Purvis, and P.D. Manning, Graphical display of musical information, Proc. XI Colloquium on Musical Informatics, Bologna, 235-236, 1995 D.J.E. Nunn, A. Purvis, and P.D. Manning, Acoustic Quanta, Proc. ICMC, Hong Kong, 52-54, 1996
  312. A.B. NuttaD, Some windows with very good sidelobe behavior, IEEE Trans. ASSP, 29, 84-87, 1981
  313. A.H. Nuttall, Efficient Evaluation of Polynomials and Exponentials of Polynomials for Equispaced Arguments, IEEE Trans. ASSP, 35(10), October 1987 H. Okamoto, Mouchu (installation), ICMC Concert Program, Hong Kong, 85, 1996
  314. H. Olson, Music, Physics, and Engineering, New York: Dover, 1967
  315. T. Oohashi et al., High frequency sound on "Trance Induction Music", Tech. Report on Musical Acoustics, Acoustical Society of Japan, ES88-77, 11-15, 1989
  316. T. Oohashi et at., High-frequency components above the audible range affects brain electric activity and sound perception, AES 91st convention preprint 3207, 1991
  317. T. Oohashi, E. Nishina, Y. Fuwamoto, and N. Kawai, On the Mechanism of "Hypersonic Effect", Proc. ICMC, Tokyo, 432-434, 1993
  318. Opcode Systems, Studio Vision Pro (sales literature), <http:// www.opcode.com/products/svpro/>, 1996
  319. P. Pabon, A real-time singing voice analysis/synthesis system, Proc. ICMC, Aarhus, 356, 1994
  320. P. Pabon, Real-time spectrumlcepstrum games, Proc. ICMC, Aarhus, 361, 1994 P.S. Pacheco, A User's Guide to MPI, <ftp://math.usfca.edu/pub/MPII mpi.guide.ps.Z>, March 1995
  321. C. Palmer, Timing in Skilled Musical Performance, PhD thesis, Cornell Univ., 1989 A. Papoulis, Signal Analysis, New York: McGraw-Hill, 1987
  322. A. Parash and U. Shimony, An expandable real-time transputer sound generator, Proc. ICMC, Montreal, 226-228, 1991
  323. W. Parsons, Separation of speech from interfering speech by means of harmonic selection, JASA, 60 (4), 911-918, 1976
  324. B. Patterson, Musical Dynamics, Scientific American, 231 ( 5): 78, 197 4
  325. R.D. Patterson, J.• Holdsworth, I. Nimmo-Smith, and P. Rice, SVOS final report: The gammatone filter bank, Cambridge Applied Psychology Unit Report 2341, 1988
  326. E.R.S. Pearson and R.G. Wilson, Multiple Event Detection from Audio Signals within a Multiresolution Framework, Proc. ICMC, Glasgow, 156-158, 1990 E.R.S. Pearson, The multiresolution Fourier transform and its application to the analysis of polyphonic music, PhD thesis, Warwick Univ., September 1991
  327. B. Pennycook, Language and Resources: A New Paradox, in The Language of Electroacoustic Music, S. Emmerson, ed., London: MacMillan Press, 1986
  328. B. Pennycook, D.R. Stammen, and D. Reynolds, Toward a Computer Model of a Jazz Improviser, Proc. ICMC, Tokyo, 228-231, 1993
  329. Perez Phillips 94 Phillips 96
  330. Pringle I. Perez, MacMUSIC, the MUSIC N environment for Macintosh, algorithmic synthesis and composition made ea.\)1, Proc. ICMC, Aarhus, 239-240, 1994
  331. D.K. Phillips, A. Purvis, and S. Johnson, A Multirate Optimisation for Real- Time Additive Synthesis, Proc. ICMC, Aarhus, 364-367, 1994
  332. D.K. Phillips, A. Purvis, and S. Johnson, Multirate Additive Synthesis, Proc. ICMC, Hong Kong, 496-499, 1996
  333. J.R. Pierce, Residues and Summation Tones-What Do We Hear?, in Current Directions in Computer Music Research, M.V. Mathews and J.R. Pierce, eds., 175-184, Cambridge, Massachusetts: MIT Press, 1991
  334. M. Piszczalski and B.F. Galler, Automatic Music Transcription, CMJ, 1(4), 24-31, Winter 1977
  335. M. Piszczalski and B.F. Galler, Predicting Musical Pitch from Component Frequency Ratios, JASA, 66, 710-720, 1979
  336. M. Piszczalski, B.F. Galler, R. Bossmeyer, M. Hatamian, and F. Looft, Performed Music: Analysis, Synthesis, and Display by Computer, J. AES, 29(1/2), 38-46, 1981
  337. Pkware Inc., PKZIP-documentation, <http://www.pkware.com/pkzip.html>, 1996 .
  338. R. Plomp, Timbre as a Multidimensional Attribute of Complex Tones,• in Frequency Analysis and Periodicity Detection in Hearing, R. P1omp and G. Smoorenburg, eds., Leiden: Sijthoff, 1970
  339. R. Plomp, Aspects of tone sensation, London: Academic Press, 1976
  340. S.T. Pope, Computer Music Workstations I Have Known and Loved, Proc. ICMC, Banff, 127-133, 1995
  341. I. Popovic, R.R. Coifman, and J. Berger, Aspects of Pitch-Tracking and Timbre Separation: Feature Detection in Digital Audio Using Adapted Local Trigonometric Bases and Wavelet Packets, Proc. ICMC, Banff, 280-283, 199 5, and <http://www. music. yale. edu/research/pc/pitchtrack. html> I. Popovic, RR Coifman, and J. Berger, Toward a Unified Representation of Sound and Analytical Structure in Music, Proc. XI Colloquium on Musical Informatics, Bologna, 55-58, 1995
  342. M.R. PortnofT, Time-scale modification of speech based on short-time Fourier analysis, IEEE Trans. ASSP, 29(3), June 1981
  343. F. Poulenc, Sonata for Hom, Trumpet, and Trombone, 1922. (audio: Record ZRG 731, London: Argo) (score: London: J.&W. Chester)
  344. J. Press, Numerical Recipes: The Art of Scientific Computing, Cambridge: Cambridge University Press, 1986, and <http://cfatab.harvard.edu/ nr/nronline.html>, 1996
  345. J. Pressing and P. Lawrence, Transcribe: A Comprehensive Autotranscription Program, Proc. ICMC, Tokyo, 343-345, 1993
  346. J. Pressing and P. Lawrence, Visualization and Predictive Modelling of Musical Signals using Embedding Techniques, Proc. ICMC, Tokyo, 110-113, 1993 R. Pringle and B.J. Ross, A Symbiosis of Animation and Music, Proc. ICMC, Hong Kong, 316-319, 1996
  347. Resynthesis, in The Psychology of Music, D. Deutsch, ed., 25-98, New York: Academic Press, 1982
  348. J-C. Risset, Paradoxical Sounds, in Current Directions in Computer Music Research, M.V. Mathews and JR. Pierce, eds., 149-158, Cambridge, Massachusetts: MIT Press, 1991
  349. C. Roads, Automated Granular Synthesis of Sound, CMJ, 2(2), 61-62, Summer 1978
  350. C. Roads, Granular Synthesis of Sound, Foundations of Computer Music, C. Roads and J. Strawn, eds., 145-159, Cambridge, Massachusetts: MIT Press, 1985 C. Roads, Introduction to Granular Synthesis, CMJ, 12(2), 11-13, Summer 1988 C. Roads, ed., lhe Music Machine-Selected Readings from Computer Music Journal, Cambridge, Massachusetts: MIT Press, 1989
  351. C. Roads, Asynchronous Granular Synthesis, in G. De Poli, A. Piccialli, and C. Roads, eds., Representations of Musical Signals, Cambridge, Massachusetts: MIT Press, 1991
  352. C. Roads, Musical Applications of Advanced Signal Transformations, Proc. Capri Workshop on Models and Representations of Musical Signals, Dept.• of Physics, Univ. ofNaples Federico II, 1992
  353. C. Roads, Musical Sound Transformation by Convolution, Proc. ICMC, Tokyo, 102-109, 1993
  354. C. Roads, Computer Music Tutorial, Cambridge, Massachusetts: MIT Press, 1994 A.J. Robinson, <ajr@softsound.com> Shorten: simple lossless and near- lossless waveform compression, <http://svr-www.eng.cam.ac.ukl-ajr/tr156/>, December 1994, and private email.
  355. K. Robinson and R.D. Patterson, lhe Duration Required To Identify the Instrument, the Octave, or the Pitch Chroma of a Musical Note, Music Perception, 13(1), 1-15, Fall1995 X.O. Rodet and Ph. DepaUe, A new additive synthesis method using inverse Fourier transform and spectral envelopes, Proc. ICMC, San Jose, 410-411, 1992 ..
  356. X.O. Rodet, Nonlinear Oscillator Models of Musical Instrument Excitation, Proc. ICMC, San Jose, 412-413, 1992
  357. X.O. Rodet and Ph. DepaUe, Spectral Envelopes and Inverse FFT Synthesis, Proc. AES Convention, 1992
  358. ' X. Rodet, Flexible Yet Controllable Physical Models: A Nonlinear Dynamics Approach, Proc. ICMC, Tokyo, 48-55, 1993
  359. X. Rodet and C. Vergez, Physical Models of Trumpet-like Instruments: Detailed Behavior and Model Improvements, Proc. ICMC, Hong Kong, 448- 453, 1996
  360. M.J. Ross, B.L. ShatTer, A. Cohen, R. Freudberg, and B.J. Manley, Average Magnitude Difference Function Pitch Extractor, IEEE Trans. ASSP, 22, 353-362, 1974
  361. Rossing Rossiter Roucous Samson Sandell91
  362. Schuck T.D. Rossing, The Science of Sound, Reading, Massachusetts: Addison- Wesley, 1990
  363. D. Rossiter and D.M. Howard, A graphical environment for electroacoustic music composition, Proc. ICMC, Aarhus, 272-275, 1994
  364. S. Roucous and A.M. Wilgus, High Quality Time-Scale Modification of Speech, IEEE Trans. ASSP, 35(10), 1486-1487, 1987
  365. P.R. Samson, Architectural issues in the design of the system concepts digital synthesizer, in Digital Audio Engineering: An anthology, J. Strawn, ed., Los Alamos: Kaufinan, 1985
  366. G.J. SandeD, A Library of Orchestral Instrument Spectra, Proc. ICMC, Montreal, 98-1 0 1, 1991
  367. G.J. Sandell, The SHARC Timbre Database, <http://www.parmly.luc.edu/ share/>, May 1995
  368. H. Sano and B.K. Jenkins, A Neural Network Model for Pitch Perception, in Music and Connectionism, P.M. Todd and D.G. Loy, eds., Cambridge, Massachusetts: MIT Press, 1991
  369. H. Sawada, N. Onoe, and S. Hashimoto, Acceleration Sensor as an Input Device for Musical Environment, Proc. ICMC, Hong Kong, 421-424, 1996 .
  370. C. Scallan and T. Stainsby, A New Software Package for Spectral Investigation and Analysis/Synthesis Using FFT and Sinusoidal Modelling Techniques, Proc. ICMC, Tokyo, 399-401, 1993
  371. G.P. Scavone, Modeling and Control of Performance Expression in Digital Waveguide Models of Woodwind Instruments, Proc. ICMC, Hong Kong, 224- 227, 1996
  372. E.D. Scheirer, Extracting Expressive Performance from Recorded Music, M.S. thesis, MIT Media Lab., 1995
  373. E.D. Scheirer, Using Musical Knowledge to Extract Expressive Performance Information from Audio Recordings, Proc. Int. Joint Conf. on Artificial Intelligence, Workshop on Computational Auditory Scene Analysis, Montreal, August 1995, and <ftp ://sound. media. mit. edu/pub!Papers/eds-ijcai95. ps. gz> E. D. Scheirer, Some thoughts on the transcription problem -or, why aren 't there good audio-to-MIDI converters available?, <http:// sound.media.mit.edu/-eds/transcription.html>, 13 January 1996
  374. W.A. Schloss, On the Automatic Transcription of Percussive Music-from Acoustics Signal to High-Level Analysis, PhD thesis, and Stanford Music Dept. Technical Report STAN-M-27, 1985
  375. W. Schottstaedt, An Introduction to FM, <http://www.notam.uio.no/ -andersvi/home/cm-sys/clm/fin. html>, 1991
  376. M.R. Schroeder, Period Histogram and Product Spectrum: New Methods for Fundamental-Frequency Extraction, JASA, 829-834, 1968
  377. O.H. Schuck and R.W. Young, Observations on the Vibrations of Piano Strings, JASA, 15, 1-11, 1943, reprinted in Musical Acoustics -Piano and Wind Instruments, E.L. Kent, ed., Stroudsburg: Dowden, Hutchinson and Ross, 1977
  378. SciTech Software, UniVesa documentation, <http://www.scitechsoft.com/>, 1995 X. Serra, A system for sound analysis/transformation/synthesis based on a deterministic plus stochastic decomposition, PhD thesis, Stanford Univ., October 1989
  379. X. Serra and J.O. Smith, Spectral Modeling Synthesis: A Sound Analysis/Synthesis System Based on a Deterministic plus Stochastic Decomposition, CMJ, 14(4), 12-24, Winter 1990
  380. X. Serra, Sound hybridization based on a deterministic plus stochastic decomposition model, Proc. ICMC, Aarhus, 348-351, 1994
  381. X. Serra, Musical Sound Modeling With Sinusoids Plus Noise, <http://www.iua.upfes!eng/recerca/mit!sms!articles!msm/>, 1996
  382. Z. Settel and C. Lippe, Musical Applications Using Real-time Frequency Domain Signal Processing, Proc. XI Colloquium on Musical Informatics, Bologna, 13-17, 1995
  383. LA. Shah and A.A. C. Kalker, Theory and design of multidimensional QMF sub-band filters from 1-D filters and polynomials using transforms, lEE Proceedings, 140(1), 67-71, 1993
  384. • R.S. Shankland and J.W. Cottman, Overtones of a vibrating string, in Musical Acoustics -Piano and Wind Instruments, E.L. Kent, ed., Dowden, Hutchinson and Ross, Stroudsburg, Pennsylvania, 1977
  385. R.N. Shephard, Structural Representations of Musical Pitch, in The Psychology of Music, D. Deutsch, ed., Academic Press, New York, 343-390, 1982 E.G. Shower and R. Biddulph, Differential Pitch Sensitivity of the Ear, JASA, 3, 274, 1931
  386. T. Shuttleworth and R.G. Wilson, Note Recognition in Polyphonic Music using Neural Networks, Dept. of Comp. Sci. Technical Report CS-RR-252, October 1993, and <ftp://ftp.dcs.warwick.ac.uk/pub/reports!rr/ 252/>
  387. S. Silberg, Intel i860 versus Digital Signal Processors (DSP), Microprocessing and Microprogramming, 35, 605-610, 1992
  388. M. Slaney, D. Ellis, and D. Rosenthal, Report on the Computational Auditory Scene Analysis Workshop, <http://sound.media.mit.edu/-dfr/casa! surnrnary.htrnl>, 1995
  389. M. Slaney, A Critique of Pure Audition, Proc. Computational Auditory Scene Analysis Workshop, Int. Joint Conf on Artificial Intelligence, Montreal, August 1995
  390. A.W. Slawson, Vowel quality and musical timbre as junctions of spectrum envelope and .fundamental frequency, JASA, 43, 87-101, 1968
  391. J.O. Smith and X. Serra, P ARSHL: An analysis/synthesis program for non- harmonic sounds based on a sinusoidal representation, Proc. ICMC, San Francisco, 290-297, 1987
  392. J.O. Smith, Viewpoints on the History of Digital Synthesis, Proc. ICMC, Montreal, 1-10, 1991
  393. J.O. Smith, Physical Modeling Using Digital Waveguides, CMJ, 16(4), 74- 91, Winter 1992
  394. Strawn Sundberg 73 Sundberg 91
  395. Swaminathan J.O. Smith, Efficient Synthesis of Stringed Musical Instruments, Proc. ICMC, Tokyo,64-71, 1993
  396. M.J.T. Smith and T.P. Barnwell, A procedure for designing exact reconstruction filterbanks for tree-structured coders, Proc. IEEE ICASSP, 27.1-4, San Diego, March 1984
  397. M.J. T. Smith and T.P. Barnwell, A unifying framework for analysis/synthesis systems based on maximally decimated filter bands, Proc. IEEE ICASSP, 521-524, Tampa, March 1985
  398. M.J. T. Smith and T.P. Barnwell, Exact Reconstruction Techniques for Tree- Structured Subband Coders, IEEE Trans. ASSP, 34(3), 434-441, 1986
  399. S.M. F. Smyth and J. V. McCanny, High-fidelity Music Coding at 4 bits, Electronic Letters, 24(8), 493-495, 1988
  400. J. Snell, Design of a Digital Oscillator That Will Generate up to 256 Low- Distortion Sine Waves in Real Time, CMJ, 1(2), 4-25, Summer 1977
  401. L. Solbach and R. Wohnnann, Sound Onset Localization and Partial Tracking in Gaussian White Noise, Proc. ICMC, Hong Kong, 324-327, 1996
  402. L. Solbach, R. Wohnnann, and J. Kliewer, The complex-valued continuous wavelet transform as a preprocessor for auditory scene analysis, in H. Okuno and D. Rosenthal, eds., Readings in Computational Auditory Scene Analysis, Hillsdale: Erlbaum, 1996, and <ftp://ftp.ti6.tu-harburg.de/pub/paper/ijcai95- casa_revl.ps.gz>, 30 January 1996
  403. M.M. Sondhi, New Methods of Pitch Extraction, IEEE Trans. Audio Electroacoustics, AU-16, 262-268, 1968
  404. R. Srinivasan, Auditory Critical Bandwidth for Short-Duration Signals, JASA, 50(2), 616-622, 1971
  405. T. Stainsby, A System for the Separation of Simultaneous Musical Audio Signals, Proc. ICMC, Hong Kong, 75-78, 1996
  406. A. Sterian and G.H. Wakefield, Robust Automated Music Transcription Systems, Proc. ICMC, Hong Kong, 219-221, 1996
  407. S.S. Stevens, The Relation of Pitch to Intensity, JASA, 6, 150, 1935
  408. T.G. Stockham, High-speed convolution and co"elation with applications to digital filtering, in Digital Processing of Signals, B. Gold and C.M. Rader, eds., New York: McGraw-Hill, 1969
  409. T.G. Stockham, T:M. Cannon, and R.B. Ingerbretsen, Blind deconvolution through digital signal processing, Proc. IEEE, 63(4), 678-692, April1975 J. Strawn, Approximation and Syntactic Analysis of Amplitude and Frequency Functions for Digital Sound Synthesis, CMJ, 4(3), 3-24, Fall 1980
  410. J.E.F. Sundberg and J. Lindqvist, Musical octaves and pitch, JASA, 54(4), 922-929, 1973
  411. J.E.F. Sundberg, The Science of Musical Sounds, San Diego: Academic Press, 1991
  412. K. Swaminathan and P.P. Yaidyanathan, Theory and Design of Uniform DFT, Parallel, Quadrature Mi"or Filters, IEEE Trans. on Circuits and Systems, 33(12), 1170-1191, December 1986 3L Ltd., Parallel C User Guide, 3L Ltd., Edinburgh, 1992
  413. N.P.McA. Todd, Wavelet Analysis of Rhythm in Expressive Musical Performance, Proc. ICMC, Tokyo, 264-267, 1993
  414. T. Todoroff, E. Daubresse, and J. Fineberg, lana -a real-time environment for analysis and extraction of frequency components of complex orchestral sounds and its application within a musical realization, Proc. ICMC, Banff, 292-293, 1995
  415. P. Toiviainen, Optimizing Self-Organizing Timbre Maps: The Effect of Auditory Images and Distance Metrics, Proc. XI Colloquium on Musical Informatics, Bologna, 141-144, 1995
  416. Transtech Ltd., TDMB416 User Guide, Transtech Ltd., High Wycombe, Buckinghamshire B. Truax, Real-Time Grarmlation of Sampled Sound with the DMX-1000, Proc. ICMC, Illinois, 1987
  417. •B. Truax, Real-Time Granular Synthesis with a Digital Signal Processor, CMJ, 12(2), 14-26, Summer 1988
  418. B. Truax, Time Shifting of Sampled Sound with a Real-Time Gram1lation Technique, Proc. ICMC, Glasgow, 104-107, 1990
  419. B. Truax, Composition with time-shifted environmental sound using a real- time granulation technique, Proc. ICMC, Montreal, 487-490, 1991
  420. B. Truax, Time Shifting and Transposition• of Sampled Sound with a Real- Time Grarmlation Technique, Proc. ICMC, Tokyo, 82-85, 1993
  421. B. Truax, Discovering Inner Complexity: Time Shifting and Transposition with a Real-time Granulation Technique, CMJ, 18(2), 38-48, Summer 1994
  422. K. Tsujimoto, M. Imai, and S. Inokuchi, Assistance Tool for Ethnic Music Recognition, Information Processing Society of Japan, 1986 [in Japanese]
  423. M. Ueda and S. Hashimoto, Blind Decomposition of Concu"ent Sounds, Proc. ICMC, Aarhus, 311-318, 1994
  424. T. Ungvary and S. Waters, The sonogram: a tool for visual documentation of musical structure, Proc. ICMC, Glasgow, 159-162, 1990
  425. P.P. Vaidyanathan, Theory and Design of M-Channel Maximally Decimated Quadrature Mi"or Filters with Arbitrary M, Having the Perfect- ReconstructionProperty, IEEE Trans. ASSP, 35(3), 1987
  426. P.P. Vaidyanaihan, Multirate digital filters, filter banks, polyphase networks, and applications: a tutorial, Proc. IEEE, 78(1), 56-93, 1990
  427. V. Vilimiiki, M. Karjalainen, and T.I. Laakso, Modeling of Woodwind Bores with Finger Holes, Proc. ICMC, Tokyo, 32-39, 1993
  428. V. Vilimiki, R. Hiinninen, and M. Karjalainen, An Improved Digital Waveguide Model of a Flute -Implementation Issues, Proc. ICMC, Hong Kong, 1-4, 1996
  429. • S.A. Van Duyne and J.O. Smith, Physical Modeling with the 2-D Digital Waveguide Mesh, Proc. ICMC, Tokyo, 40-47, •1993
  430. S.A. Van Duyne and J.O. Smith, The 3D Tetrahedral Digital Waveguide Mesh with Musical Applications, Proc. ICMC, Hong Kong, 9-16, 1996
  431. R. Van Klitzing and A. Kohlrausch, Effect of masker level on overshoot in running-and frozen-noise maskers, JASA, 95(4), 2192-2201, 1994
  432. B. Vercoe, The Synthetic Performer in the context of Live Performance, Proc. ICMC, Paris, 199-200, 1984
  433. B. Vercoe and M. Puckette, Synthetic Rehearsal: Training the Synthetic Performer, Proc. ICMC, Vancouver, 275-278, 1985
  434. B. Vercoe and D; Cumming, Connectionist Machine Tracking of Polyphonic Audio, Proc. ICMC, Cologne, 2ri-218, 1988
  435. B. Vercoe and D.P.W. EUis, Real-time Csound: Software Synthesis with Sensing and Control, Proc. ICMC, Glasgow, 209-211, 1990
  436. B. Vercoe, Csound: A Manual for the Audio Processing System and Supporting Programs with Tutorials, Media Laboratory, MIT, 1993
  437. B. Vercoe, Extended Csound, Proc. ICMC, Hong Kong, 141-142, 1996
  438. M.-P. Verge, Physical modeling of aeroacoustic sources in flute-/ike musical instruments, Proc. ICMC, Hong Kong, 5-8, 1996
  439. G. von Bekesy, Experiments in Hearing, New York: McGraw-Hill, 1960
  440. G. von Bismarck, Timbre of steady sounds: A factorial investigation of its verba/.attributes, Acustica, 30, 146-159, 1974
  441. S. Wake, H. Kato, N. Saiwaki, and S. Inokuchi, The Session System Reacting to the Sentiment of the Player, Japan Music and Computer Science Society, Proc. of Summer Symposium, 1992 [in Japanese]
  442. S. Wake, H. Kato, N. Saiwaki, and S. Inokuchi, Cooperative musical partner system using tension parameter: JASPER Oam session partner), Trans. IPS Japan, 35(7), 1469-1481, 1994 [in Japanese]
  443. D. WallratT, The DMX-1000 Signal Processing Computer, <;:MJ, 3(4), 44-49, Winter 1979
  444. A. Wang, Instantaneous and Frequency-Warped Signal Processing Techniques for Auditory Source Separation, PhD thesis, Dept. ofEiec. Engg., Stanford Univ., Stanford, California, August 1994
  445. W.D. Ward and E.M. Bums, Absolute Pitch, in The Psychology of Music, D. Deutsch, ed., New York: Academic Press, 431-451, 1982
  446. C. Watson, The Computer Analysis of Polyphonic Music, PhD thesis, Sydney Univ., 1986
  447. J. Wawrzynek, C. Mead, L. Tzu-mu, and L. Dyer, A VLSI approach to sound synthesis, Proc. ICMC, Paris, 53-64, 1984 Wawrzynek 91
  448. Zwicker J. Wawnynek, VLSI Models for Sound Synthesis, in Current Directions in Computer Music Research, M.V. Mathews and J.R. Pierce, eds., 185-198, Cambridge, Massachusetts: MIT Press, 1991
  449. M. Weintraub, A Theory and Computational Model of Auditory Monaural Sound Separation, PhD thesis, Dept. ofEiec. Eng., Stanford Univ., 1985
  450. L.G. Weiss, Wavelets and Wideband Correlation Processing, IEEE Signal Processing Magazine, 13-32, January 1994
  451. D.L. Wessel, Low dimensional control of musical timbre, Proc. 59th Conv. AES, Hamburg, 1978
  452. D.L. Wessel, Timbre space as a musical control structure, CMJ, 3(2), 45-52, Summer 1979
  453. M.V. Wickerhauser, Acoustic Signal Compression with Wave Packets, <http :1 /www.math. yale.edu/pub/wavelets/papers/acoustic. tex>, 1989
  454. G. Widmer, Learning Expressive Performance: The Structure-Level Approach, JNMR, 25(2), 179-205, June 1996
  455. G. Wiggins, E. Miranda, A. Smaill, and M. Harris, A Framework for the Evaluation of Music Representation Systems, CMJ, 17(3), 31-42, Fall 1993
  456. Wildcat Canyon Software, Autoscore, <http://www.wildcat.com/Pages/ Autoscor.htm>, 1996
  457. R. Williamson <76570.2752@compuserve.com>, Video for DOS, <gopher:// rnicros.hensa.ac.uk:70/11/rnicros/ibmpc/dosll/l095/>, January 1995
  458. R.G. Wilson, A.D. Calway, and E.R.S. Pearson, A Generalized Wavelet Transform for Fourier Analysis: The Multiresolution Fourier Transform and Its Application to Image and Audio Signal Analysis, IEEE Trans. Information Theory, 38(2), 674-690, March 1992
  459. R.G. Wilson, A.D. Calway, E.R.S. Pearson, and A.R. Davies, An Introduction to the Multiresolution Fourier Transform and Its Applications, Warwick Univ. Dept. of Comp. Sci. Technical Report 204, January 1992, and <ftp :/ /ftp. des. warwick.ac. ~pub/reports/rr/204/>
  460. F. Winckel, Measurements of the Acoustic Effectiveness and Quality of Trained Singers' Voices, Proc. 90th meeting Acoust. Soc. of America, San Francisco, 1975
  461. R. Wohrmann and L. Solbach, Preprocessing for the Automated Transcription of P_olyphonic Music: Linking Wavelet Theory and Auditory Filtering; Proc. ICMC, Banff, 396-399, 1995
  462. A. Wood, The Physics of Music, London: Chapman and Hall, 1976
  463. L Xenakis, Formalized Music, Bloomington: Indiana University Press, 1971 (and Pen dragon, 1991)
  464. Yamaha Corporation, Yamaha SY77 Operating Manual, Hamamatsu, Japan E. Zwicker, G. Flottorp, and S.S. Stevens, Critical Bandwidth in Loudness Summation, JASA, 29, 548, 1957 11.4 Company addresses American Paper Optics American Paper Optics, 2005 Nonconnah Boulevard, Suite 27, Memphis. TN 38132, USA
  465. Atlanta Atlanta Sisnal Processors Inc .. 1375 Peaehtn:c St. NE, Suile 690, Atlanta, GA 30309-3115, USA Borland Borland International Inc., 1800 Green Hills Road, PO Box 660001, Seotts Valley, CA 95066•000 1, USA
  466. Emagie Emagic Inc., 13348 Grass Valley Ave., Bldg C, Suite 100, Gross Valley, CA 95945, USA Gravis Advanced Gravis Computer Tcelmology Ud., 101-3750 North Fraser Way, Burnaby, B.C. V5J 5E9, Canada I Hohner Hohner Midia, SchwabbcnstraBe 27, 0.74626 Bretzfeld, Gennany Jordan Hargrapbics Jordan Hargraphies Software, 8760-A Research Boulevard #256, Austin, TX 78758, USA LT soonll LT Sound Inc., 7980 LT Parlcway, Lithonia, GA 30058, USA Mauusblta Matsushita Eleetrie Industrial Co. Ud., Ceotral P.O. Box 288, Osaka 53G-91, Japan Microsoft Microsoft Inc., 1 Microsoft Way, Redmond, WA 98052~399, USA Opcodc Opcodc Systems Inc., 3950 Fabian Way, Suite 100, Palo Alto, CA 94303, USA Pkware Pkware Inc., 91025 North Deenvood Drive, Brown Deer, WI 53223, USA SeiTeeh SeiTech Software Inc., 5 ~Lane, SuiteD, Chieo, CA 9~926•1989, USA Syntrillium Syntrillium Software C01p., P.O. Box 60274, Pbocoix, AZ 85082-0274, USA Texas ft>struments Texas lnstrumcnulnc., 12501 Research Boulevard, Austin, TX 78759, USA 3L 3L Ltd., 86/92 Causcwayside, Edinburgh EHI 1PY, UK 'Tr~pstech Transteeh Ltd., 17•19 Manor Court Yard, Hugbcnden Avenue, High Wycombe, Bue c, HP13 5RE, UK
  467. Wildcat Wildeot Canyon Softwan:, 1563 Solano Aveoue #264, Berkeley, CA 94707, USA Yarnaba Yamaba C01p0<ation, Nakauwa-<:ho 1G-I, Hamamatsu, Japan 430
  468. The Graphics Interchange Format is the copyright property of CompuServe Incorporated. GIF is a "Service Mark property of CompuServe Incorporated. PCX files are PC Paintbrush format iJ11118es. PC Paintbrush is published by Z.Soft. T ARGA is a registered trademark of Truevision Incorporated. Microsoft, Windows, and MS-DOS are trademarks of Microsoft Corporation. UNIX is a trademark of AT&T Bell Laboratories. All other produet names menrioned are rrademarks or registered trademarks oftheir respecJ:ive owners.
  469. D.J.E. Nunn, • A. Purvis, and P.D. Manning, Source Separation and Transcription of Polyphonic Music, Proc. International Colloquium on New Music Research, Gent, Belgium, 1994
  470. D.J .E. Nunn, A. Purvis, and P.D. Manning, Graphical display of musical information, Proc. XI Colloquium on Musical Informatics, Bologna, 235-236, 1995 T. ltagaki, D.J.E Nuna, D.K. Phillips, D. Batjakis, A. Purvis, and P.D. Manning, Activity Report, XI Colloquium on Musical Informatics, Bologna, 51-54, 1995
  471. D.J.E. Nun• , A. Purvis, and P.D. Manniag, ACQustic Quanta. Proc. ICMC, Hong Kong, 52-54, 1996
  472. T. ltagaki, S. JohMOn, P.D. Manning, D.J.E. Nunn, D.K. Phillips, A. Purvis, and J. Spanier, Durham Music Tech110logy: Activity Report, Proc. ICMC, Hong Kong, 126•128, 1996

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