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Extracting Noise-Robust Features From Audio Data
Overview A key problem faced by audio identification, classification, and retrieval systems is the mapping of high-dimensional audio input data into informative lower-dimensional feature vectors. This paper explores an automatic dimensionality reduction algorithm called Distortion Discriminant Analysis (DDA). Each layer of DDA projects its input into directions which maximize the SNR for a given set of distortions. Multiple layers efficiently extract features over a wide temporal window. The audio input to DDA undergoes perceptually-relevant preprocessing and de-equalization, to further suppress distortions. DDA is applied to the task of identifying audio clips in an incoming audio stream, based on matching stored audio fingerprints.
| Publisher | Institute of Electrical and Electronics Engineers | File Format | |
|---|---|---|---|
| Date Published | April 2002 | ||
| Format | White Papers | ||
| Topics | |||
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