Exploratory analysis and visualization of speech and music by locally linear embedding

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voice recognition
speech recognition
audio processing
signal processing
pattern recognition
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Many problems in voice recognition and audio processing involve feature extraction from raw waveforms. The goal of feature extraction is to reduce the dimensionality of the audio signal while preserving the informative signatures that, for example, distinguish different phonemes in speech or identify particular instruments in music. If the acoustic variability of a data set is described by a small number of continuous features, then we can imagine the data as lying on a low dimensional manifold in the high dimensional space of all possible waveforms. Locally linear embedding (LLE) is an unsupervised learning algorithm for feature extraction in this setting. In this paper, we present results from the exploratory analysis and visualization of speech and music by LLE.

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Copyright © 2004 IEEE. Reprinted from Proceedings of the 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2004), held 17-24 May 2004, Montreal, Quebec, Canada. Publisher URL: http://ieeexplore.ieee.org/xpl/tocresult.jsp?isNumber=29345&page=17 This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
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