IEICE TRANSACTIONS on Information
Pitch Estimation and Voicing Classification Using Reconstructed Spectrum from MFCC
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E101-D No.2 pp.556-559
- Publication Date
- 2018/02/01
- Publicized
- 2017/11/15
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2017EDL8162
- Type of Manuscript
- LETTER
- Category
- Speech and Hearing
Authors
JianFeng WU
Hangzhou Dianzi University
HuiBin QIN
Hangzhou Dianzi University
YongZhu HUA
Hangzhou Dianzi University
LingYan FAN
Hangzhou Dianzi University
Keyword
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JianFeng WU, HuiBin QIN, YongZhu HUA, LingYan FAN, "Pitch Estimation and Voicing Classification Using Reconstructed Spectrum from MFCC" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 2, pp. 556-559, February 2018, doi: 10.1587/transinf.2017EDL8162.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017EDL8162/_p
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@ARTICLE{e101-d_2_556,
author={JianFeng WU, HuiBin QIN, YongZhu HUA, LingYan FAN, },
journal={IEICE TRANSACTIONS on Information},
title={Pitch Estimation and Voicing Classification Using Reconstructed Spectrum from MFCC},
year={2018},
volume={E101-D},
number={2},
pages={556-559},
abstract={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.},
keywords={},
doi={10.1587/transinf.2017EDL8162},
ISSN={1745-1361},
month={February},}
Copy
TY - JOUR
TI - Pitch Estimation and Voicing Classification Using Reconstructed Spectrum from MFCC
T2 - IEICE TRANSACTIONS on Information
SP - 556
EP - 559
AU - JianFeng WU
AU - HuiBin QIN
AU - YongZhu HUA
AU - LingYan FAN
PY - 2018
DO - 10.1587/transinf.2017EDL8162
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2018
AB - 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.
ER -
English
