TY - JOUR
T1 - Robust Bayesian Pitch Tracking Based on the Harmonic Model
AU - Shi, L.
AU - Nielsen, J. K.
AU - Jensen, J. R.
AU - Little, M. A.
AU - Christensen, M. G.
PY - 2019/11
Y1 - 2019/11
N2 - Fundamental frequency is one of the most important characteristics of speech and audio signals. Harmonic model-based fundamental frequency estimators offer a higher estimation accuracy and robustness against noise than the widely used autocorrelation-based methods. However, the traditional harmonic model-based estimators do not take the temporal smoothness of the fundamental frequency, the model order, and the voicing into account as they process each data segment independently. In this paper, a fully Bayesian fundamental frequency tracking algorithm based on the harmonic model and a first-order Markov process model is proposed. Smoothness priors are imposed on the fundamental frequencies, model orders, and voicing using first-order Markov process models. Using these Markov models, fundamental frequency estimation and voicing detection errors can be reduced. Using the harmonic model, the proposed fundamental frequency tracker has an improved robustness to noise. An analytical form of the likelihood function, which can be computed efficiently, is derived. Compared to the state-of-the-art neural network and nonparametric approaches, the proposed fundamental frequency tracking algorithm has superior performance in almost all investigated scenarios, especially in noisy conditions. For example, under 0 dB white Gaussian noise, the proposed algorithm reduces the mean absolute errors and gross errors by 15% and 20% on the Keele pitch database and 36% and 26% on sustained /a/ sounds from a database of Parkinson's disease voices. A MATLAB version of the proposed algorithm is made freely available for reproduction of the results.
1
1An implementation of the proposed algorithm using MATLAB may be found in https://tinyurl.com/yxn4a543.
AB - Fundamental frequency is one of the most important characteristics of speech and audio signals. Harmonic model-based fundamental frequency estimators offer a higher estimation accuracy and robustness against noise than the widely used autocorrelation-based methods. However, the traditional harmonic model-based estimators do not take the temporal smoothness of the fundamental frequency, the model order, and the voicing into account as they process each data segment independently. In this paper, a fully Bayesian fundamental frequency tracking algorithm based on the harmonic model and a first-order Markov process model is proposed. Smoothness priors are imposed on the fundamental frequencies, model orders, and voicing using first-order Markov process models. Using these Markov models, fundamental frequency estimation and voicing detection errors can be reduced. Using the harmonic model, the proposed fundamental frequency tracker has an improved robustness to noise. An analytical form of the likelihood function, which can be computed efficiently, is derived. Compared to the state-of-the-art neural network and nonparametric approaches, the proposed fundamental frequency tracking algorithm has superior performance in almost all investigated scenarios, especially in noisy conditions. For example, under 0 dB white Gaussian noise, the proposed algorithm reduces the mean absolute errors and gross errors by 15% and 20% on the Keele pitch database and 36% and 26% on sustained /a/ sounds from a database of Parkinson's disease voices. A MATLAB version of the proposed algorithm is made freely available for reproduction of the results.
1
1An implementation of the proposed algorithm using MATLAB may be found in https://tinyurl.com/yxn4a543.
KW - Harmonic analysis
KW - Mathematical model
KW - Hidden Markov models
KW - Frequency estimation
KW - Computational modeling
KW - Bayes methods
KW - Speech processing
KW - Fundamental frequency or pitch tracking
KW - harmonic model
KW - Markov process
KW - harmonic order
KW - voiced-unvoiced detection
UR - http://www.scopus.com/inward/record.url?scp=85070575284&partnerID=8YFLogxK
U2 - 10.1109/TASLP.2019.2930917
DO - 10.1109/TASLP.2019.2930917
M3 - Journal article
SN - 2329-9290
VL - 27
SP - 1737
EP - 1751
JO - IEEE/ACM Transactions on Audio, Speech, and Language Processing
JF - IEEE/ACM Transactions on Audio, Speech, and Language Processing
IS - 11
M1 - 8771212
ER -