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A Large-Scale Evaluation of Acoustic and Subjective Music Similarity Measures

Adam Berenzweig; Beth Logan; Daniel P. W. Ellis; Brian Whitman

Title:
A Large-Scale Evaluation of Acoustic and Subjective Music Similarity Measures
Author(s):
Berenzweig, Adam
Logan, Beth
Ellis, Daniel P. W.
Whitman, Brian
Date:
Type:
Articles
Department:
Electrical Engineering
Permanent URL:
Book/Journal Title:
ISMIR 2003
Publisher:
International Symposium on Music Information Retrieval
Abstract:
Subjective similarity between musical pieces and artists is an elusive concept, but one that must be pursued in support of applications to provide automatic organization of large music collections. In this paper, we examine both acoustic and subjective approaches for calculating similarity between artists, comparing their performance on a common database of 400 popular artists. Specifically, we evaluate acoustic techniques based on Mel-frequency cepstral coefficients and an intermediate 'anchor space' of genre classification, and subjective techniques which use data from The All Music Guide, from a survey, from playlists and personal collections, and from web-text mining. We find the following: (1) Acoustic-based measures can achieve agreement with ground truth data that is at least comparable to the internal agreement between different subjective sources. However, we observe significant differences between superficially similar distribution modeling and comparison techniques. (2) Subjective measures from diverse sources show reasonable agreement, with the measure derived from co-occurrence in personal music collections being the most reliable overall. (3) Our methodology for largescale cross-site music similarity evaluations is practical and convenient, yielding directly comparable numbers for different approaches. In particular, we hope that our information-retrieval-based approach to scoring similarity measures, our paradigm of sharing common feature representations, and even our particular dataset of features for 400 artists, will be useful to other researchers.
Subject(s):
Electrical engineering
Artificial intelligence
Item views:
52
Metadata:
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