Robust Fundamental Frequency Estimation in Coloured Noise
| Autor: | Alfredo Esquivel Jaramillo, Jesper Kjar Nielsen, Andreas Jakobsson, Mads Grasboll Christensen |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Signal processing
coloured noise Estimator 020206 networking & telecommunications 02 engineering and technology Filter (signal processing) Fundamental frequency Least squares 030507 speech-language pathology & audiology 03 medical and health sciences Noise LCMV filter Minimum-variance unbiased estimator 0202 electrical engineering electronic engineering information engineering pre-whitening fundamental frequency maximum likelihood 0305 other medical science Algorithm least-squares Mathematics Parametric statistics |
| Zdroj: | Esquivel Jaramillo, A, Jakobsson, A, Nielsen, J K & Christensen, M G 2020, Robust Fundamental Frequency Estimation in Coloured Noise . in 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 ., 9053018, IEEE, I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings, 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020, Barcelona, Spain, 04/05/2020 . https://doi.org/10.1109/ICASSP40776.2020.9053018 ICASSP |
| DOI: | 10.1109/ICASSP40776.2020.9053018 |
| Popis: | Most parametric fundamental frequency estimators make the implicit assumption that any corrupting noise is additive, white Gaus-sian. 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 in most of the evaluated scenarios. |
| Databáze: | OpenAIRE |
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