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Improvements of Continuous Model for Memory-based Automatic Music Transcription
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SYSNO ASEP 0347257 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Improvements of Continuous Model for Memory-based Automatic Music Transcription Author(s) Albrecht, Š. (CZ)
Šmídl, Václav (UTIA-B) RID, ORCIDSource Title Proceedings of the 18th European signal processing conference. - Aalborg : Eurasip, 2010 - ISSN 2076-1465 Pages s. 487-491 Number of pages 5 s. Publication form www - www Action European signal processing conference Event date 23.07.2010-27.07.2010 VEvent location Aalborg Country DK - Denmark Event type WRD Language eng - English Country DK - Denmark Keywords music transcription ; extended Kalman filter Subject RIV BD - Theory of Information R&D Projects GP102/08/P250 GA ČR - Czech Science Foundation (CSF) CEZ AV0Z10750506 - UTIA-B (2005-2011) Annotation Automatic music transcription is a process recovering the most likely combination of sounds that produced the recorded audio signal. We are concerned with memory-based approach, where the observed signal is modeled as a superposition of sounds from a library. Moreover, we assume that only parts of the sounds can be played. The number of possible combinations is excessive and exact estimation is computationally prohibitive. We propose to transform the original discrete-event model into a less restricted parametrization and impose the constraints in a soft way via prior information. The resulting model is a non-linear state-space model with Gaussian disturbances. The posterior estimates are evaluated by the extended Kalman filter. Performance of the model is studied in simulation and it is shown that it outperforms previously published methods. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2011
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