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In this paper, we describe a novel audio feature extraction method, which can effectively improve the performance of music identification under noisy circumstances. It is based on a dual box approach that extracts from the sound spectrogram point clusters with significant energy variation. This approach was tested in a song finder application that can identify music from samples recorded by microphone in the presence of dominant noise. A series of experiments show that under noisy circumstances, our system outperforms current state-of-the-art music identification algorithms and provides very good precision, scalability and query efficiency.
Author (s): Bourguet, Marie-Luce; Wang, Jiajun
Affiliation:
Beijing University of Posts and Telecommunications, Beijing, China; Queen Mary University of London, London, UK
(See document for exact affiliation information.)
AES Convention: 129
Paper Number:8180
Publication Date:
2010-11-06
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Session subject:
Emerging Applications
Permalink: https://aes2.org/publications/elibrary-page/?id=15603
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Bourguet, Marie-Luce; Wang, Jiajun; 2010; A Robust Audio Feature Extraction Algorithm for Music Identification [PDF]; Beijing University of Posts and Telecommunications, Beijing, China; Queen Mary University of London, London, UK; Paper 8180; Available from: https://aes2.org/publications/elibrary-page/?id=15603
Bourguet, Marie-Luce; Wang, Jiajun; A Robust Audio Feature Extraction Algorithm for Music Identification [PDF]; Beijing University of Posts and Telecommunications, Beijing, China; Queen Mary University of London, London, UK; Paper 8180; 2010 Available: https://aes2.org/publications/elibrary-page/?id=15603