With the growing amount of multimedia data available everywhere and the necessity to provide efficient methods for browsing and indexing this plethora of audio content, automated musical similarity search and retrieval has gained considerable attention in recent years. This paper presents a system which combines a set of perceptual low level features with appropriate classification strategies for the task of retrieving similar sounding songs in a database. A method for analyzing the classification results while avoiding time consuming subjective listening tests for an optimum feature selection and combination is presented. It is based on a calculated ''similarity index'' which reflects the similarity between specifically embedded smilarity pairs. The system's performance as well as the usefulness of the analyzing method is evaluated by a subjective listening test.

Authors: Kastner, Thorsten; Herre, Juergen; Allamanche, Eric; Hellmuth, Oliver; Ertel, Christian; Schalek, Marion
Affiliation: Fraunhofer Institute for Integrated Circuits, Erlangen, Germany
AES Conference: 25th International Conference: Metadata for Audio (June 2004)
Paper Number: 5-3
Publication Date: June 1, 2004
Subject: Metadata for Audio

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