Multipitch estimation of piano sounds using a new probabilistic spectral smoothness principle

Valentin Emiya 1 Roland Badeau 2 Bertrand David 2
1 METISS - Speech and sound data modeling and processing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : A new method for the estimation of multiple concurrent pitches in piano recordings is presented. It addresses the issue of overlapping overtones by modeling the spectral envelope of the overtones of each note with a smooth autoregressive model. For the background noise, a moving-average model is used and the combination of both tends to eliminate harmonic and sub-harmonic erroneous pitch estimations. This leads to a complete generative spectral model for simultaneous piano notes, which also explicitly includes the typical deviation from exact harmonicity in a piano overtone series. The pitch set which maximizes an approximate likelihood is selected from among a restricted number of possible pitch combinations as the one. Tests have been conducted on a large homemade database called MAPS, composed of piano recordings from a real upright piano and from high-quality samples.
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Valentin Emiya, Roland Badeau, Bertrand David. Multipitch estimation of piano sounds using a new probabilistic spectral smoothness principle. IEEE Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2010, 18 (6), pp.1643-1654. ⟨http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5356234⟩. ⟨10.1109/TASL.2009.2038819⟩. ⟨inria-00510392⟩

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