Departmental Papers (CIS)

Document Type

Conference Paper

Date of this Version

May 2004

Comments

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

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Abstract

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.

Keywords

voice recognition, speech recognition, audio processing, signal processing, pattern recognition, acoustics

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Date Posted: 27 July 2004

This document has been peer reviewed.