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

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 statis- tical update formulas are obtained by the variational Bayesian in- ference. Compared with the conventional group LASSO-type algo- rithms for multi-pitch estimation, experimental results indicate ro- bustness against noise and improved estimation accuracy of the pro- posed method.