For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
Pitch Estimation and Voicing Classification Using Reconstructed Spectrum from MFCC
JianFeng WU HuiBin QIN YongZhu HUA LingYan FAN
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2018/02/01
Online ISSN: 1745-1361
Type of Manuscript: LETTER
Category: Speech and Hearing
pitch estimation, voicing classification, MFCC, GMM,
Full Text: PDF(355.7KB)>>
In this paper, a novel method for pitch estimation and voicing classification is proposed using reconstructed spectrum from Mel-frequency cepstral coefficients (MFCC). The proposed algorithm reconstructs spectrum from MFCC with Moore-Penrose pseudo-inverse by Mel-scale weighting functions. The reconstructed spectrum is compressed and filtered in log-frequency. Pitch estimation is achieved by modeling the joint density of pitch frequency and the filter spectrum with Gaussian Mixture Model (GMM). Voicing classification is also achieved by GMM-based model, and the test results show that over 99% frames can be correctly classified. The results of pitch estimation demonstrate that the proposed GMM-based pitch estimator has high accuracy, and the relative error is 6.68% on TIMIT database.