Pitch Estimation and Tracking with Harmonic Emphasis On The Acoustic Spectrum

Sam Karimian-Azari, Nasser Mohammadiha, Jesper Rindom Jensen, Mads Græsbøll Christensen

Publikation: Bidrag til tidsskriftKonferenceartikel i tidsskriftForskningpeer review

5 Citationer (Scopus)
560 Downloads (Pure)

Abstrakt

In this paper, we use unconstrained frequency estimates (UFEs) from a noisy harmonic signal and propose two methods to estimate and track the pitch over time. We assume that the UFEs are multivariate-normally-distributed random variables, and derive a maximum likelihood (ML) pitch estimator by maximizing the likelihood of the UFEs over short time-intervals. As the main contribution of this paper, we propose two state-space representations to model the pitch continuity, and, accordingly, we propose two Bayesian methods, namely a hidden Markov model and a Kalman filter. These methods are designed to optimally use the correlations in the consecutive pitch values, where the past pitch estimates are used to recursively update the prior distribution for the pitch variable. We perform experiments using synthetic data as well as a noisy speech recording, and show that the Bayesian methods provide more accurate estimates than the corresponding ML methods.
OriginalsprogEngelsk
TidsskriftI E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings
Sider (fra-til)4330-4334
ISSN1520-6149
DOI
StatusUdgivet - apr. 2015
Begivenhed40th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2015 - Brisbane, Australien
Varighed: 19 apr. 201524 apr. 2015
Konferencens nummer: 2015

Konference

Konference40th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2015
Nummer2015
LandAustralien
ByBrisbane
Periode19/04/201524/04/2015

Fingeraftryk Dyk ned i forskningsemnerne om 'Pitch Estimation and Tracking with Harmonic Emphasis On The Acoustic Spectrum'. Sammen danner de et unikt fingeraftryk.

Citationsformater