Temporally-constrained convolutive probabilistic latent component analysis for multi-pitch detection

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Abstract

In this paper, a method for multi-pitch detection which exploits the temporal evolution of musical sounds is presented. The proposed method extends the shift-invariant probabilistic latent component analysis algorithm by introducing temporal constraints using multiple Hidden Markov Models, while supporting multiple-instrument spectral templates. Thus, this model can support the representation of sound states such as attack, sustain, and decay, while the shift-invariance across log-frequency can be utilized for multi-pitch detection in music signals that contain frequency modulations or tuning changes. For note tracking, pitch-specific Hidden Markov Models are also employed in a postprocessing step. The proposed system was tested on recordings from the RWC database, the MIREX multi-F0 dataset, and on recordings from a Disklavier piano. Experimental results using a variety of error metrics, show that the proposed system outperforms a non-temporally constrained model. The proposed system also outperforms state-of-the art transcription algorithms for the RWC and Disklavier datasets. © 2012 Springer-Verlag.

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APA

Benetos, E., & Dixon, S. (2012). Temporally-constrained convolutive probabilistic latent component analysis for multi-pitch detection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7191 LNCS, pp. 364–371). https://doi.org/10.1007/978-3-642-28551-6_45

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