Fundamental Frequency Estimation in Speech Signals With Variable Rate Particle Filters

Zhang, Geliang; Godsill, Simon

doi:10.1109/taslp.2016.2531285

Cited by 12 publications

(7 citation statements)

References 18 publications

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“…Using (9), (12), (13), (14), (19) and (20), a closed-form marginal likelihood can be obtained, i.e., p(y n |ẍ n , Y n−1 )…”

Section: Pitch Trackingmentioning

confidence: 99%

“…A sample-bysample Kalman filtering-based pitch tracking algorithm using a time-varying harmonic model is proposed in [18] by assuming that the pitch and weights follow first-order Markov chains. A particle filtering-based pitch tracking algorithm based on the source-filter speech model combining with the harmonic modelling of input source is introduced in [19]. However, the arXiv:1905.08557v1 [cs.SD] 21 May 2019 P r e p r i n t good performance of the algorithms in [18] and [19] requires careful initializations.…”

Section: Introductionmentioning

confidence: 99%

“…A particle filtering-based pitch tracking algorithm based on the source-filter speech model combining with the harmonic modelling of input source is introduced in [19]. However, the arXiv:1905.08557v1 [cs.SD] 21 May 2019 P r e p r i n t good performance of the algorithms in [18] and [19] requires careful initializations. Moreover, it is difficult to integrate the time-varying model order into these algorithms, see [20] as an example of combing discrete and continuous state spaces.…”

Section: Introductionmentioning

confidence: 99%

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Robust Bayesian Pitch Tracking Based on the Harmonic Model

Shi

Nielsen

Jensen

et al. 2019

IEEE/ACM Trans. Audio Speech Lang. Process.

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Fundamental frequency is one of the most important characteristics of speech and audio signals. Harmonic modelbased 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 non-parametric 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 .

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“…Using (9), (12), (13), (14), (19) and (20), a closed-form marginal likelihood can be obtained, i.e., p(y n |ẍ n , Y n−1 )…”

Section: Pitch Trackingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust Bayesian Pitch Tracking Based on the Harmonic Model

Shi

Nielsen

Jensen

et al. 2019

IEEE/ACM Trans. Audio Speech Lang. Process.

View full text Add to dashboard Cite

show abstract

“…In addition to the instantaneous signal processing methods mentioned above, various machine learning approaches that utilise generative models, such as a Gaussian mixture model (GMM) and hidden Markov models (HMMs) [14,15,16,17], have been developed along with particle filters [18,19] to address the challenge related to severe noisy conditions. In this context, models based on deep neural networks (DNNs) have shown promising achievement in tackling the problem [12,20,21] because of the explicit capability of DNNs for complex pattern mapping as a discriminative model.…”

Section: Introductionmentioning

confidence: 99%

A Regression Model of Recurrent Deep Neural Networks for Noise Robust Estimation of the Fundamental Frequency Contour of Speech

Kato¹,

Kinnunen²

2018

The Speaker and Language Recognition Workshop (Odyssey 2018)

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The fundamental frequency (F 0) contour of speech is a key aspect to represent speech prosody that finds use in speech and spoken language analysis such as voice conversion and speech synthesis as well as speaker and language identification. This work proposes new methods to estimate the F 0 contour of speech using deep neural networks (DNNs) and recurrent neural networks (RNNs). They are trained using supervised learning with the ground truth of F 0 contours. The latest prior research addresses this problem first as a frame-byframe-classification problem followed by sequence tracking using deep neural network hidden Markov model (DNN-HMM) hybrid architecture. This study, however, tackles the problem as a regression problem instead, in order to obtain F 0 contours with higher frequency resolution from clean and noisy speech. Experiments using PTDB-TUG corpus contaminated with additive noise (NOISEX-92) show the proposed method improves gross pitch error (GPE) by more than 25 % at signal-tonoise ratios (SNRs) between-10 dB and +10 dB as compared with one of the most noise-robust F 0 trackers, PEFAC. Furthermore, the performance on fine pitch error (FPE) is improved by approximately 20 % against a state-of-the-art DNN-HMMbased approach.

show abstract

Section: Introductionmentioning

confidence: 99%

A Regression Model of Recurrent Deep Neural Networks for Noise Robust Estimation of the Fundamental Frequency Contour of Speech

Kato¹,

Kinnunen²

2018

Preprint

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The fundamental frequency (F 0) contour of speech is a key aspect to represent speech prosody that finds use in speech and spoken language analysis such as voice conversion and speech synthesis as well as speaker and language identification. This work proposes new methods to estimate the F 0 contour of speech using deep neural networks (DNNs) and recurrent neural networks (RNNs). They are trained using supervised learning with the ground truth of F 0 contours. The latest prior research addresses this problem first as a frame-byframe-classification problem followed by sequence tracking using deep neural network hidden Markov model (DNN-HMM) hybrid architecture. This study, however, tackles the problem as a regression problem instead, in order to obtain F 0 contours with higher frequency resolution from clean and noisy speech.Experiments using PTDB-TUG corpus contaminated with additive noise (NOISEX-92) show the proposed method improves gross pitch error (GPE) by more than 25 % at signal-tonoise ratios (SNRs) between -10 dB and +10 dB as compared with one of the most noise-robust F 0 trackers, PEFAC. Furthermore, the performance on fine pitch error (FPE) is improved by approximately 20 % against a state-of-the-art DNN-HMMbased approach.

show abstract

Fundamental Frequency Estimation in Speech Signals With Variable Rate Particle Filters

Cited by 12 publications

References 18 publications

Robust Bayesian Pitch Tracking Based on the Harmonic Model

Robust Bayesian Pitch Tracking Based on the Harmonic Model

A Regression Model of Recurrent Deep Neural Networks for Noise Robust Estimation of the Fundamental Frequency Contour of Speech

A Regression Model of Recurrent Deep Neural Networks for Noise Robust Estimation of the Fundamental Frequency Contour of Speech

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