TY - GEN
T1 - Robust Fundamental Frequency Estimation in Coloured Noise
AU - Esquivel Jaramillo, Alfredo
AU - Jakobsson, Andreas
AU - Nielsen, Jesper Kjær
AU - Christensen, Mads Græsbøll
PY - 2020
Y1 - 2020
N2 - Most parametric fundamental frequency estimators make the implicit assumption that any corrupting noise is additive, white Gaussian. Under this assumption, the maximum likelihood (ML) and the least squares estimators are the same, and statistically efficient. However, in the coloured noise case, the estimators differ, and the spectral shape of the corrupting noise should be taken into account. To allow for this, we here propose two schemes that refine the noise statistics and parameter estimates in an iterative manner, one of them based on an approximate ML solution and the other one based on removing the periodic signal obtained from a linearly constrained minimum variance (LCMV) filter. Evaluations on real speech data indicate that the iteration steps improve the estimation accuracy, therefore offering improvement over traditional non-parametric fundamental frequency methods.
AB - Most parametric fundamental frequency estimators make the implicit assumption that any corrupting noise is additive, white Gaussian. Under this assumption, the maximum likelihood (ML) and the least squares estimators are the same, and statistically efficient. However, in the coloured noise case, the estimators differ, and the spectral shape of the corrupting noise should be taken into account. To allow for this, we here propose two schemes that refine the noise statistics and parameter estimates in an iterative manner, one of them based on an approximate ML solution and the other one based on removing the periodic signal obtained from a linearly constrained minimum variance (LCMV) filter. Evaluations on real speech data indicate that the iteration steps improve the estimation accuracy, therefore offering improvement over traditional non-parametric fundamental frequency methods.
KW - fundamental frequency
KW - coloured noise
KW - maximum likelihood
KW - least-squares
KW - LCMV filter
KW - pre-whitening
UR - https://www.youtube.com/watch?v=wt90AEtHz_g
UR - https://sigport.org/documents/robust-fundamental-frequency-estimation-coloured-noise
UR - https://github.com/alfredoesquivelaudioaau/iterativeF0Ar_NLS
U2 - 10.1109/ICASSP40776.2020.9053018
DO - 10.1109/ICASSP40776.2020.9053018
M3 - Article in proceeding
SN - 978-1-5090-6632-2
T3 - I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings
BT - 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
PB - IEEE
T2 - 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
Y2 - 4 May 2020 through 8 May 2020
ER -