Automatic Music Similarity Measures
, Hewlett Packard
Date: Friday, October 07, 2005
Time: 1:30 PM to 2:30 PM
Refreshments: 3:15 PM
Location: Patil Seminar Room (32-G449)
Host: Jaime Teevan, CSAIL
Contact: Jaime Teevan, 617/253-1611, email@example.com
Speaker URL: None
It would be an incredible understatement so say that there have been large changes in the music industry in recent years. We are moving toward a future in which anyone can publish music and expect it to be available to everybody. In addition we can expect all previously published music to be accessible online. Improved search techniques will be needed to enable consumers to find music of interest in these vast music repositories. Automatic determination of similarity between artists and songs is at the core of such algorithms since it provides a scalable way to index and recommend music.
In this talk we describe several efforts to automatically determine similarity between artists and songs. The first is based on acoustic properties of music and the second is based on analysis of the lyrics. Results on the uspop2002 database show that acoustic-based similarity outperforms that based on lyrics. However the errors made by each technique are not randomly distributed suggesting that the two techniques could be profitably combined.
A sub-theme of this presentation will be evaluation techniques in the emerging field of music information retrieval.
Beth Logan received the BSc. and B.E. degrees from the University of Queensland, Australia, in 1990 and 1991 respectively. She received the PhD in engineering from the University of Cambridge, United Kingdom, in 1998, completing a dissertation on speech enhancement. Since 1998, she has been a research scientist at Hewlett Packard's Cambridge Research Laboratory in Cambridge Massachusetts. Her work here has focused on indexing of speech and music, medical informatics and computational biology. http://www.hpl.hp.com/personal/Beth_Logan/
Created by Linda L. Julien at Wednesday, June 19, 2013 at 6:21 AM.