A fast pitch tracking algorithm using the harmonic model.

The article for this work is available in PUBLISHED, PREPRINT

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

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.

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

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

Liming Shi, Aalborg university, Email: ls@create.aau.dk