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ICASSP
2011
IEEE

Sparse coding of auditory features for machine hearing in interference

8 years 10 months ago
Sparse coding of auditory features for machine hearing in interference
A key problem in using the output of an auditory model as the input to a machine-learning system in a machine-hearing application is to find a good feature-extraction layer. For systems such as PAMIR (passive–aggressive model for image retrieval) that work well with a large sparse feature vector, a conversion from auditory images to sparse features is needed. For audio-file ranking and retrieval from text queries, based on stabilized auditory images, we took a multi-scale approach, using vector quantization to choose one sparse feature in each of many overlapping regions of different scales, with the hope that in some regions the features for a sound would be stable even when other interfering sounds were present and affecting other regions. We recently extended our testing of this approach using sound mixtures, and found that the sparse-coded auditory-image features degrade less in interference than vector-quantized MFCC sparse features do. This initial success suggests that our ...
Richard F. Lyon, Jay Ponte, Gal Chechik
Added 20 Aug 2011
Updated 20 Aug 2011
Type Journal
Year 2011
Where ICASSP
Authors Richard F. Lyon, Jay Ponte, Gal Chechik
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