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A Local Feature Aggregation Method for Music Retrieval
Jin S. SEO
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2018/01/01
Online ISSN: 1745-1361
Type of Manuscript: Special Section LETTER (Special Section on Enriched Multimedia — Potential and Possibility of Multimedia Contents for the Future —)
music retrieval, music search, music information retrieval, supervector,
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The song-level feature summarization is an essential building block for browsing, retrieval, and indexing of digital music. This paper proposes a local pooling method to aggregate the feature vectors of a song over the universal background model. Two types of local activation patterns of feature vectors are derived; one representation is derived in the form of histogram, and the other is given by a binary vector. Experiments over three publicly-available music datasets show that the proposed local aggregation of the auditory features is promising for music-similarity computation.