1. A computer-implemented method comprising: generating a collection of auditory images including a plurality of audio images from each of a plurality of audio files, each auditory image being generated from respective audio files according to an auditory model; for each audio file, extracting sparse features from each auditory image generated for the audio file to generate a sparse feature vector representing the audio file, wherein extracting sparse features from each auditory image comprises: dividing the auditory image into multiple sub-images, applying a feature extractor to each sub-image to generate corresponding local sparse codes, and combining the sparse codes from each sub-image to form a sparse vector for the auditory image; and ranking the audio files in response to a query including one or more words using the sparse feature vectors and a matching function relating sparse feature vectors to words in the query.
2. The method of claim 1 , where the matching function is generated using a training collection of annotated audio files, and where generating the matching function includes: receiving the collection of annotated audio files, each annotated audio file having an auditory image and one or more keywords associated with the content of the audio file; generating a sparse feature vector for each audio file in the collection; and training the matching function using the sparse feature vectors and the one or more keywords for the collection of annotated audio files to determine a matrix of weights matching sparse features and keywords.