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 Import into BibTeX
Subject: Metadata for Audio
Permalink: https://www.aes.org/e-lib/browse.cfm?elib=12815

Click to purchase paper as a non-member or login as an AES member. If your company or school subscribes to the E-Library then switch to the institutional version. If you are not an AES member and would like to subscribe to the E-Library then Join the AES!

This paper costs $33 for non-members and is free for AES members and E-Library subscribers.

Learn more about the AES E-Library

E-Library Location: (CD 25thPapers)   /25/aes25-000045.pdf

Start a discussion about this paper!