Multipitch detection for piano music: Benchmarking a few approaches

Abstract : When trying to find a solution to the critical and sometimes ill-posed problem of multipitch estimation, it is common to have to choose between several approaches: Using a processing that resembles that of the human auditory perception, or using a decomposition adapted to the spectral content of the targeted sound category, taking into account an a priori knowledge of the spectral properties of musical notes or trying to learn some of its charateristics or even more, to run an algorithm that blindly tends to separate the sound into multiple elementary entities. This work involves some recently published techniques, such as the non-negative matrix factorization with sparsity constraints, a likelihood approach, based on a smooth spectral envelope model for both the background noise and for the partials, and a deterministic method combining spectral and temporal criteria. Their performance is comparatively assessed on a common multipitch database restricted to piano music, drawn both from home-made recordings and soft-synthesized sounds. The results are discussed with respect to the complexity, the versatility, the sensitivity to fine tuning, and the precision reached by each approach.
Type de document :
Communication dans un congrès
154th Meeting of the Acoustical Society of America, Nov 2007, New Orleans, United States. 2007, 〈10.1121/1.2942555〉
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Contributeur : Valentin Emiya <>
Soumis le : jeudi 9 décembre 2010 - 15:00:09
Dernière modification le : jeudi 11 janvier 2018 - 06:23:38

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Bertrand David, Roland Badeau, Nancy Bertin, Valentin Emiya, Gaël Richard. Multipitch detection for piano music: Benchmarking a few approaches. 154th Meeting of the Acoustical Society of America, Nov 2007, New Orleans, United States. 2007, 〈10.1121/1.2942555〉. 〈inria-00545065〉

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