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

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International Conference on Speech, Acoustics, and Signal Processing. 2004 May 17;3:iii - 984-7

Jain V, Saul LK

Note: Research in this publication was not performed at Janelia.

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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.

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

Note: Research in this publication was not performed at Janelia.

Abstract