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All Projects → LimingShi → Bayesian-Pitch-Tracking-Using-Harmonic-model

LimingShi / Bayesian-Pitch-Tracking-Using-Harmonic-model

Licence: GPL-2.0 license
Pitch detection and pitch tracking, voicing unvoicing detection (VAD)，基音检测

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Labels

onset-detectionpitch-detectionpitch-estimationspeech-analysisvad-detectionvoicing-unvoicing-detectionfundamental-frequency-estimation

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Bayesian Pitch Tracking Using Harmonic model

A fast pitch tracking algorithm using the harmonic model.

The article for this work is available in PUBLISHED, PREPRINT

How to run

This project contains both the MATLAB and CPP code

For the MATLAB code:

Run run_white_example.m (white Gaussian noise) or run_colored_example.m (factory noise) in MATLAB in the BF0NLS_MATLAB folder

Run main.m in MATLAB in the BF0NLS_realtimeDemo_MATLAB folder

Examples

Figure 1: Pitch estimates for speech signals under 0 dB white Gaussian noise (Running time on my laptop is around 2.6 s).

Figure 2: Pitch estimates for speech signals under 0 dB factory noise (Running time on my laptop is around 9.3 s, and prewhitening is used).

Figure 3: Pitch estimates for music signals (vibrato flute sound) under 0 dB white Gaussian noise (Running time on my laptop is around 32.2 s).

How to cite

L. Shi, J. K. Nielsen, J. R. Jensen, M. A. Little, and M. G. Chris- tensen, “Robust bayesian pitch tracking based on the harmonic model,” IEEE/ACM Trans. Audio, Speech, and Lang. Process., vol. 27, no. 11, pp. 1737–1751, Nov 2019.

References

This fast computation of the likelihood function is based on the fast pitch estimation method proposed in

Fast fundamental frequency estimation: Making a statistically efficient estimator computationally efficient. Nielsen, Jesper Kjær; Jensen, Tobias Lindstrøm; Jensen, Jesper Rindom; Christensen, Mads Græsbøll; Jensen, Søren Holdt. In: Signal Processing, 135, 2017, pp. 188-197.

Bayesian Model Comparison With the g-Prior. Nielsen, Jesper Kjær; Christensen, Mads Græsbøll; Cemgil, Ali Taylan; Jensen, Søren Holdt. In: IEEE Transactions on Signal Processing, 62 (1), 2014, pp. 225-238.

where the source code is available in https://github.com/jkjaer/fastF0Nls

This noise PSD tracker used for prewhitening is based on the method proposed in

Gerkmann, T. & Hendriks, R. C. Unbiased MMSE-Based Noise Power Estimation With Low Complexity and Low Tracking Delay, IEEE Trans Audio, Speech, Language Processing, 2012, 20, 1383-1393

where the source code is available in http://www.ee.ic.ac.uk/hp/staff/dmb/voicebox/voicebox.html

Questions

If you have any question regarding to the theory and code, feel free to contact

Liming Shi, Aalborg university, Email: [email protected]

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