Content-based retrieval of music and audio

Though many systems exist for content-based retrieval of images, little work has been done on the audio portion of the multimedia stream. This paper presents a system to retrieve audio documents by acoustic similarity. The similarity measure is based on statistics derived from a supervised vector quantizer, rather than matching simple pitch or spectral characteristics. The system is thus able to learn distinguishing audio features while ignoring unimportant, variation. Both theoretical and experimental results are presented, including quantitative measures of retrieval performance. Retrieval was tested on a corpus of simple sounds as well as a corpus of musical excerpts. The system is purely data-driven and does not depend on particular audio characteristics. Given a suitable parameterization, this method may thus be applicable to image retrieval as well.