Pitch Estimation and Tracking with Harmonic Emphasis On The Acoustic Spectrum

Research output: Contribution to journalConference article in Journal

Abstract

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.
Close

Details

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.
Original languageEnglish
JournalI E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings
Pages (from-to)4330-4334
ISSN1520-6149
DOI
StatePublished - Apr 2015
Publication categoryResearch
Peer-reviewedYes
Event40th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2015 - Brisbane, Australia
Duration: 19 Apr 201524 Apr 2015
Conference number: 2015

Conference

Conference40th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2015
Number2015
CountryAustralia
CityBrisbane
Period19/04/201524/04/2015

    Research areas

  • Harmonic signal, frequency estimate, pitch estimation, Bayesian filter, Kalman filter

Download statistics

No data available
ID: 207893238