Searching musical audio datasets by a batch of multi-variant tracks

Authors:
Yi Yu;J. Stephen Downie;Lei Chen;Vincent Oria;Kazuki Joe
Affiliations:
Nara Women's University, Nara, Japan;University of Illinois at Urbana-Champaign, Champaign, IL, USA;Hong Kong University of Science and Technology, Hong Kong, China;New Jersey Institute of Technology, Newark, NJ, USA;Nara Women's University, Nara, Japan
Venue:
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Year:
2008

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Abstract

Multi-variant music tracks are those audio tracks of a particular song which are sung and recorded by different people (i.e., cover songs). As music social clubs grow on the Internet, more and more people like to upload music recordings onto such music social sites to share their own home-produced albums and participate in Internet singing contests. Therefore it is very important to explore a computer-assisted evaluation tool to detect these audio-based multi-variant tracks. In this paper we investigate such a task: the original track of a song is embedded in datasets, with a batch of multi-variant audio tracks of this song as input, our retrieval system returns an ordered list by similarity and indicates the position of relevant audio track. To help process multi-variant audio tracks, we suggest a semantic indexing framework and propose the Federated Features (FF) scheme to generate the semantic summarization of audio feature sequences. The conjunction of federated features with three typical similarity searching schemes, K-Nearest Neighbor (KNN), Locality Sensitive Hashing (LSH), and Exact Euclidian LSH (E2LSH), is evaluated. From these findings, a computer-assisted evaluation tool for searching multi-variant audio tracks was developed to search over large musical audio datasets.