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Computer Science

Audio Signal Processing

Profile image of Preeti RaoPreeti Rao

2008, Studies in Computational Intelligence

https://doi.org/10.1007/978-3-540-75398-8_8
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21 pages

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Abstract
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This paper explores the field of audio signal processing, emphasizing the importance of auditory scene analysis and its applications in various domains such as speech recognition, music transcription, and multimedia data retrieval. It highlights key aspects of digital audio signal processing, including audio classification, compression methods, and the characteristics of audio signals crucial for effective processing. The discussion also covers time-frequency representations and feature extraction pertinent to audio classification systems.

Figures (4)
Fig. 1. The auditory field in the frequency-intensity plane. The sound pressure level is measured in dB with respect to the standard reference pressure level of 20 microPascals.
Fig. 1. The auditory field in the frequency-intensity plane. The sound pressure level is measured in dB with respect to the standard reference pressure level of 20 microPascals.
Table 1. A description of the audio events corresponding to Figure 1. The spectrogram by means of its time-frequency analysis displays the spectro-temporal properties of acoustic events that may overlap in time and frequency. The choice of the analysis window duration dictates the trade-off between the frequency resolution of steady-state content versus the time res- olution of rapidly time-varying events or transients.
Table 1. A description of the audio events corresponding to Figure 1. The spectrogram by means of its time-frequency analysis displays the spectro-temporal properties of acoustic events that may overlap in time and frequency. The choice of the analysis window duration dictates the trade-off between the frequency resolution of steady-state content versus the time res- olution of rapidly time-varying events or transients.
Fig. 2. (a) Waveform, and (b) spectrogram of the audio segment described in Ta- ble 1. The vertical dotted lines indicate the starting instants of new events. The spectrogram relative intensity scale appears at lower right. While the spectrogram and auditory signal representations discussed in the previous section are good for visualization of audio content, they have a high dimensionality which makes them unsuitable for direct application to clas- sification. Ideally, we would like to extract low-dimensional features from these representations (or even directly from the acoustical signal) which retain only the important distinctive characteristics of the intended audio classes. Reduced-dimension, decorrelated spectral vectors obtained using a inear transformation of a spectrogram have been proposed in MPEG-7, the audiovisual content description standard [5], [6].
Fig. 2. (a) Waveform, and (b) spectrogram of the audio segment described in Ta- ble 1. The vertical dotted lines indicate the starting instants of new events. The spectrogram relative intensity scale appears at lower right. While the spectrogram and auditory signal representations discussed in the previous section are good for visualization of audio content, they have a high dimensionality which makes them unsuitable for direct application to clas- sification. Ideally, we would like to extract low-dimensional features from these representations (or even directly from the acoustical signal) which retain only the important distinctive characteristics of the intended audio classes. Reduced-dimension, decorrelated spectral vectors obtained using a inear transformation of a spectrogram have been proposed in MPEG-7, the audiovisual content description standard [5], [6].
Fig. 3. Audio feature extraction procedure (adapted from [8]).
Fig. 3. Audio feature extraction procedure (adapted from [8]).

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