@inproceedings{acb43ee33f8d47a9997d7f25e13cf8aa,
title = "Multi-Pitch Estimation and Tracking Using Bayesian Inference in Block Sparsity",
abstract = "In this paper, we consider the problem of multi-pitch estimation and tracking of an unknown number of harmonic audio sources. The regularized least-squares is a solution for simultaneous sparse source selection and parameter estimation. Exploiting block sparsity, the method allows for reliable tracking of the found sources, without posing detailed a priori assumptions of the number of harmonics for each source. The method incorporates a Bayesian prior and assigns data-dependent regularization coefficients to efficiently incorporate both earlier and future data blocks in the tracking of estimates. In comparison with fix regularization coefficients, the simulation results, using both real and synthetic audio signals, confirm the performance of the proposed method.",
keywords = "Multi-pitch estimation, tracking, harmonic signal, regularized least-squares, sparsity",
author = "Sam Karimian-Azari and Andreas Jakobsson and Jensen, {Jesper Rindom} and Christensen, {Mads Gr{\ae}sb{\o}ll}",
year = "2015",
doi = "10.1109/EUSIPCO.2015.7362336",
language = "English",
isbn = "978-0-9928626-3-3",
series = "Proceedings of the European Signal Processing Conference",
publisher = "IEEE",
pages = "16--20",
booktitle = "2015 Proceedings of the 23rd European Signal Processing Conference (EUSIPCO 2015)",
address = "United States",
note = "European Signal Processing Conference (EUSIPCO) ; Conference date: 31-08-2015 Through 04-09-2015",
}