Sound retrieval and ranking using sparse auditory representations

Richard F. Lyon, Martin Rehn, Samy Bengio, Thomas C. Walters, Gal Chechik

Research output: Contribution to journalLetterpeer-review

44 Scopus citations


To create systems that understand the sounds that humans are exposed to in everyday life, we need to represent sounds with features that can discriminate among many different sound classes. Here, we use a sound-ranking framework to quantitatively evaluate such representations in a large-scale task. We have adapted a machine-vision method, the passive-aggressive model for image retrieval (PAMIR), which efficiently learns a linear mapping from a very large sparse feature space to a large query-term space. Using this approach, we compare different auditory front ends and different ways of extracting sparse features from high-dimensional auditory images. We tested auditory models that use an adaptive pole-zero filter cascade (PZFC) auditory filter bank and sparsecode feature extraction from stabilized auditory images with multiple vector quantizers. In addition to auditory image models, we compare a family of more conventional mel-frequency cepstral coefficient (MFCC) front ends. The experimental results show a significant advantage for the auditory models over vector-quantized MFCCs. When thousands of sound files with a query vocabulary of thousands of words were ranked, the best precision at top-1 was 73% and the average precision was 35%, reflecting a 18% improvement over the best competingMFCC front end.

Original languageEnglish
Pages (from-to)2390-2416
Number of pages27
JournalNeural Computation
Issue number9
StatePublished - Sep 2010
Externally publishedYes


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