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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 91 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 8.6.5.9.5 Shephard rhythms but finite density, i.e. Q({set}, 0, large, {set}). This

Figure 131 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 91 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 8.6.5.9.5 Shephard rhythms but finite density, i.e. Q({set}, 0, large, {set}). This

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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.
windscreen wipers or a squeaky gate hinge. The printer noise shown in Figure 2 is in a twelve-tone scale and a fairly regular tempo — the low E is the
windscreen wipers or a squeaky gate hinge. 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.
Figure 9 - Responses of six fibres in the auditory nerve of a cat.
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.
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.
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 distant
Table 5 - Dynamic ranges of several instruments. a distance of 50 metres, and another singing pianissimo at 1 metre. The listener will judge the distant
range, there are about 30 JNDs in one critical band. The table below summarises the results “”"** The resolution is poorer at lower frequencies, and is never better than 2 Hz. Throughout most of th
range, there are about 30 JNDs in one critical band. The table below summarises the results “”"** 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, =xamples. Loman *4 Plomp concludes that the maximum effect i
{Licklider, Plomp 76, Van Klitzing, =xamples. Loman *4 Plomp concludes that the maximum effect i
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
2.4.6.4 Effects of timbre on separation
2.4.6.4 Effects of timbre on separation
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 classif
However, transcription of polyphonic music is particularly difficult because before we can classif
bandwidth, and we wish to determine the frequencies involved. However, music covers at least ter The FFT is commonly used for frequency analysis where the range of interest covers a relatively small
bandwidth, and we wish to determine the frequencies involved. However, music covers at least ter The FFT is commonly used for frequency analysis where the range of interest covers a relatively small
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!*isset 82) illustrates the framework for analysis and resynthesis
e 29, after Risset!*isset 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./""°** ° 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 reproduce the exact nuances of a sound is compromised when such approximations are made.
generation, the following table shows how many operations are required per partial per sample./""°** ° 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 reproduce the exact nuances of a sound is compromised when such approximations are made.
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 woson 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 woson 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
Figure 32. order to investigate the possibilities of real-time synthesis. The hardware is shown schematically i
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"> 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"> 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
performance analysis. In many cases the aims fall into several categories. The table below summarises most of the polyphonic systems above. EPA stands for expressive
performance analysis. In many cases the aims fall into several categories. The 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
6.4.6 Filter timing
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.
and plots a histogram of the energy separately for each octave. It also prints out a characterisation file Once we have the spectra from each octave, we then try to determine the sinusoids contained in it. This The next program, called DISTRIBx®3, 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
and plots a histogram of the energy separately for each octave. It also prints out a characterisation file Once we have the spectra from each octave, we then try to determine the sinusoids contained in it. This The next program, called DISTRIBx®3, 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 auditory elements’ of Brown,/O™"S “4! This is done by linking sines in neighbouring blocks using a 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 auditory elements’ of Brown,/O™"S “4! This is done by linking sines in neighbouring blocks using a 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
virtual memory. We then carry out an exhaustive search, looking for simultaneous frequencies that are close to being harmonically related. A partial at (n’xfy) is taken to be the n™ partial of fo if |n’/n] is le
virtual memory. We then carry out an exhaustive search, looking for simultaneous frequencies that are close to being harmonically related. A partial at (n’xfy) is taken to be the n™ partial of fo if |n’/n] is le
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.
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 7.2.2.6 Duration and time resolution notes. The spectra, shown in Figure 74, show that this is indeed the case. The first two notes are long
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 7.2.2.6 Duration and time resolution notes. The spectra, shown in Figure 74, show that this is indeed the case. The first two notes are long
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.
sets of images such as those below. This produces an animation of ‘the effect of altering t programs used to animate the output of the spectral display program. This can also be applied to other
sets of images such as those below. This produces an animation of ‘the effect of altering t programs used to animate the output of the spectral display program. This can also be applied to other
illustrated in Figure 76. below shows how many were picked out, and how many partial tracks were formed; the data is
illustrated in Figure 76. below shows how many were picked out, and how many partial tracks were formed; the data is
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.
7.2.2.8 Recognition of quiet notes The number of partial tracks is shown in the table below and in Figure 79. The correct numbers are 7618 Pe ee ae Ҥ AML 2nus.pl-n
7.2.2.8 Recognition of quiet notes The number of partial tracks is shown in the table below and in Figure 79. The correct numbers are 7618 Pe ee ae Ҥ AML 2nus.pl-n
of 6 bits is equivalent to a halving of amplitude.
of 6 bits is equivalent to a halving of amplitude.
sines and 405 tracks.
sines and 405 tracks.
have ONE partial — i.e. the fundamental.
have ONE partial — i.e. the fundamental.
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 these coincide, corresponding to ‘correct’ transcription, is shown in dark grey — this corresponds to th clear that with a lower threshold more possible notes are removed, but many are still removed for being
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 these coincide, corresponding to ‘correct’ transcription, is shown in dark grey — this corresponds to th clear that with a lower threshold more possible notes are removed, but many are still removed for being
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
seconds of the Sonata III for organ written by Felix Mendelssohn between August 1844 and January example 3))). All audio examples are listed in Appendix Q. It will be noted that the current system does 845 ,!Mendetssohn] The audio was created in mono 16-bit linear format at 32 kHz using Csound files
seconds of the Sonata III for organ written by Felix Mendelssohn between August 1844 and January example 3))). All audio examples are listed in Appendix Q. It will be noted that the current system does 845 ,!Mendetssohn] 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. 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. 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. Notes added from harmonics each track is viewed as potentially a note, and notes may share partials.
Table 31 - Comparison of comparison methods. neither evaluated or conveyed. Notes added from harmonics each track is viewed as potentially a note, and notes may share partials.
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. iming.'“°P! The aim was to determine which of the pianists it was. The spectrum is shown in Figure 98
As the aim was deduction of timing information, a smaller FFT, of size 32, was used. iming.'“°P! The aim was to determine which of the pianists it was. The spectrum is shown in Figure 98
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
Figure 102 - Spectrum of start of Grieg Piano Concerto. ano polyphony is often 8, and has a maximum of 17 during the arpeggio in bar 4 played with the There are 166 notes in this excerpt, not including the initial timpani roll and orchestra hit. The wnitter
Figure 102 - Spectrum of start of Grieg Piano Concerto. ano polyphony is often 8, and has a maximum of 17 during the arpeggio in bar 4 played with the There are 166 notes in this excerpt, not including the initial timpani roll and orchestra hit. The wnitter
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
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
minimum note length of 0.5 seconds. shows the flattened comparison. [he 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. shows the flattened comparison. [he 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:- from the spectrum, the odd harmonics are considerably stronger than the even harmonics. This is to be at G#3, E#4, etc., corresponding to the third, fifth, etc. harmonics of the fundamental. As can be seen
It is very simple to form the ideal score. The quantitative evaluation is as follows:- from the spectrum, the odd harmonics are considerably stronger than the even harmonics. This is to be at G#3, E#4, etc., corresponding to the third, fifth, etc. harmonics of the fundamental. As can be seen
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. 8.6.2.3 Multiplication and convolution
When two atoms are multiplied, the resultant species is given by the following table. 8.6.2.3 Multiplication and convolution
frequency is effectively lowered.85 Roads also discusses FIR filtering by convolution.""°*5 *
frequency is effectively lowered.85 Roads also discusses FIR filtering by convolution.""°*5 *
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
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.
% Moorer “!) Slawson cites research showing fixed formant for violins and double-reed woodwin
% Moorer “!) Slawson cites research showing fixed formant for violins and double-reed woodwin
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
Xc are intermediate results, and Xs is the spectrum data.
Xc are intermediate results, and Xs is the spectrum data.
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 As this is independent of the period P, we will normalise it using P=1s. This gives the final result:-
convolved with a periodic input spectrum As this is independent of the period P, we will normalise it using P=1s. This gives the final result:-
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.
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 Oxo!
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 Oxo!
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
‘able 48 - List of audio examples These audio examples can be found on the enclosed cassette tape and on the World-Wide Web at the
‘able 48 - List of audio examples These audio examples can be found on the enclosed cassette tape and on the World-Wide Web at the
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