Dictionary learning for pitch estimation in speech signals

This paper presents an automatic approach for parameter training for a sparsity-based pitch estimation method that has been previously published. For this pitch estimation method, the harmonic dictionary is a key parameter that needs to be carefully prepared beforehand. In the original method, extensive human supervision and involvement are required to construct and label the dictionary. In this study, we propose to employ dictionary learning algorithms to learn the dictionary directly from training data. We apply and compare 3 typical dictionary learning algorithms, i.e., the method of optimized directions (MOD), K-SVD and online dictionary learning (ODL), and propose a post-processing method to label and adapt a learned dictionary for pitch estimation. Results show that MOD and properly initialized ODL (pi-ODL) can lead to dictionaries that exhibit the desired harmonic structures for pitch estimation, and the post-processing method can significantly improve performance of the learned dictionaries in pitch estimation. The dictionary obtained with pi-ODL and post-processing attained pitch estimation accuracy close to the optimal performance of the manual dictionary. It is positively shown that dictionary learning is feasible and promising for this application.