Multipitch Estimation Using Block Sparse Bayesian Learning and Intra-Block Clustering

Liming Shi; Jesper Rindom Jensen; Jesper Kjær Nielsen; Mads Græsbøll Christensen

ICASSP 2018accepted0 citations

Multipitch Estimation Using Block Sparse Bayesian Learning and Intra-Block Clustering

Liming Shi, Jesper Rindom Jensen, Jesper Kjær Nielsen, Mads Græsbøll Christensen

Abstract

Pitch estimation is an important task in speech and audio analysis. In this paper, we present a multi-pitch estimation algorithm based on block sparse Bayesian learning and intra-block clustering for speech analysis. A statistical hierarchical model is formulated based on a pitch dictionary with a fixed maximum number of harmonics for all the candidate pitches. Block sparse Bayesian learning is proposed for estimating the complex amplitudes. To deal with the problem of unknown harmonic orders and subharmonic errors, intra-block clustering structured sparsity prior is also introduced. The statistical update formulas are obtained by the variational Bayesian inference. Compared with the conventional group LASSO-type algorithms for multi-pitch estimation, experimental results indicate robustness against noise and improved estimation accuracy of the proposed method.

BibTeX

@inproceedings{icassp2018_multipitchestima,
  title = {Multipitch Estimation Using Block Sparse Bayesian Learning and Intra-Block Clustering},
  author = {Liming Shi and Jesper Rindom Jensen and Jesper Kjær Nielsen and Mads Græsbøll Christensen},
  booktitle = {ICASSP 2018},
  year = {2018}
}