DocumentCode :
2182709
Title :
Sparse coding of auditory features for machine hearing in interference
Author :
Lyon, Richard F. ; Ponte, Jay ; Chechik, Gal
fYear :
2011
fDate :
22-27 May 2011
Firstpage :
5876
Lastpage :
5879
Abstract :
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 hope of robustness in interference may indeed be realizable, via the general idea of sparse features that are localized in a domain where signal components tend to be localized or stable.
Keywords :
feature extraction; image retrieval; learning (artificial intelligence); vector quantisation; PAMIR; audio file ranking; feature extraction; machine hearing; machine learning system; passive-aggressive model for image retrieval; sound mixture; sparse coded auditory image feature; sparse coding; vector quantization; Encoding; Interference; Mel frequency cepstral coefficient; Sparse matrices; Testing; Training; Vector quantization; Auditory image; PAMIR; sound retrieval and ranking; sparse code;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on
Conference_Location :
Prague
ISSN :
1520-6149
Print_ISBN :
978-1-4577-0538-0
Electronic_ISBN :
1520-6149
Type :
conf
DOI :
10.1109/ICASSP.2011.5947698
Filename :
5947698
Link To Document :
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