COSIN: content-based retrieval system for cover songs

Authors:
Yi Yu;J. Stephen Downie;Fabian Moerchen;Lei Chen;Kazuki Joe;Vincent Oria
Affiliations:
Nara Women's University, Nara, Japan;University of Illinois at Urbana-Champaign, Champaign, IL, USA;Siemens Corporate Research, Princeton, NJ, USA;Hong Kong University of Science and Technology, Hong Kong, China;Nara Women's University, Nara, Japan;New Jersey Institute of Technology, Newark, NJ, USA
Venue:
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Year:
2008

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Abstract

We develop a content-based audio COver Song IdeNtification (COSIN) system to detect/group cover songs. The COSIN takes music audio content as input and performs similarity searching to locate variants of the input (i.e., cover versions). Identified cover songs are returned in the rank order according to their similarity to the input. The COSIN also incorporates a set of tools to evaluate retrieval performance so researchers can explore different retrieval schemes and parameters (e.g. recall, precision). The COSIN utilizes a suite of techniques to detect cover songs including: Pitch + Dynamic Programming (DP), Chroma + DP, and Semantic Feature Summarization (SFS) + Hash-Based Approximate Matching (HBAM). Demonstration system shows that COSIN is a very potential music content retrieval tool. Running some music retrieval schemes on COSIN platform, recent experiments with SFS + LSH Variants demonstrate a nicely balanced efficiency (search speed) v. performance (search accuracy) tradeoff.