Continuous Wavelet-like Transform Based Music Similarity Features for Intelligent Music Navigation

Aliaksandr Paradzinets 1 Oleg Kotov Hadi Harb 1 Liming Chen 1
1 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : Intelligent music navigation is one of the important tasks in today's music applications. In this context we propose several high-level musical similarity features that can be used in automatic music navigation, classification and recommendation. The features we propose use Continuous Wavelet-like Transform as a basic time-frequency analysis of a musical signal due to its flexibility in time-frequency resolutions. A novel 2D beat histogram is presented in the paper as a rhythmic similarity feature which is free from dependency on recording condition and does not require sophisticated adaptive algorithms of threshold finding in beat detection. This paper also describes a CWT based algorithm of multiple F0 estimation (note detection) and corresponding melodic similarity features. Evaluation of the both similarity measures is done in automatic genre classification context and playlist composition.
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Poster communications
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https://hal.archives-ouvertes.fr/hal-01589802
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Submitted on : Tuesday, September 19, 2017 - 10:13:20 AM
Last modification on : Thursday, November 21, 2019 - 2:31:21 AM

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Aliaksandr Paradzinets, Oleg Kotov, Hadi Harb, Liming Chen. Continuous Wavelet-like Transform Based Music Similarity Features for Intelligent Music Navigation. International Workshop on Content-Based Multimedia Indexing, CBMI '07, Jun 2007, Bordeaux, France. IEEE, 2007, ⟨10.1109/CBMI.2007.385407⟩. ⟨hal-01589802⟩

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