AES E-Library

Using Audio Classifiers as a Mechanism for Content-Based Song Similarity

As collections of digital music become larger and more widespread, there is a growing need for assistance in a user's navigation and interaction with a collection and with the individual members of that collection. Examining pairwise song relationships and similarities, based upon content derived features, provides a useful tool to do so. This paper looks into a means of extending a song classification algorithm to provide song to song similarity information. In order to evaluate the effectiveness of this method, the similarity data is used to group the songs into k-means clusters, these clusters are then compared against the original genre sorting algorithm.

Authors: Fields, Benjamin; Casey, Michael
Affiliation: Goldsmiths College, University of London
AES Convention:123 (October 2007) Paper Number:7267 Import into BibTeX
Subject:Applications in Audio

Click to purchase paper 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 $20 for non-members, $5 for AES members and is free for E-Library subscribers.

Learn more about the AES E-Library

E-Library Location: (CD 123Papers)   /123/7267.pdf

Start a discussion about this paper!