A Kalman-based fundamental frequency estimation algorithm

Liming Shi, Jesper K. Nielsen, Jesper R. Jensen, Max A. Little, Mads G. Christensen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Fundamental frequency estimation is an important task in speech and audio analysis. Harmonic model-based methods typically have superior estimation accuracy. However, such methods usually assume that the fundamental frequency and amplitudes are stationary over a short time frame. In this paper, we propose a Kalman filter-based fundamental frequency estimation algorithm using the harmonic model, where the fundamental frequency and amplitudes can be truly nonstationary by modeling their time variations as firstorder Markov chains. The Kalman observation equation is derived from the harmonic model and formulated as a compact nonlinear matrix form, which is further used to derive an extended Kalman filter. Detailed and continuous fundamental frequency and amplitude estimates for speech, the sustained vowel /a/ and solo musical tones with vibrato are demonstrated.

Original languageEnglish
Title of host publication2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2017
PublisherIEEE
Pages314-318
Number of pages5
Volume2017-October
ISBN (Electronic)9781538616321
DOIs
Publication statusPublished - 11 Dec 2017
Event2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2017 - New Paltz, United States
Duration: 15 Oct 201718 Oct 2017

Conference

Conference2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2017
CountryUnited States
CityNew Paltz
Period15/10/1718/10/17

Fingerprint

Frequency estimation
Extended Kalman filters
Kalman filters
Markov processes

Bibliographical note

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Keywords

  • extended Kalman filter
  • Fundamental frequency estimation
  • harmonic model

Cite this

Shi, L., Nielsen, J. K., Jensen, J. R., Little, M. A., & Christensen, M. G. (2017). A Kalman-based fundamental frequency estimation algorithm. In 2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2017 (Vol. 2017-October, pp. 314-318). IEEE. https://doi.org/10.1109/WASPAA.2017.8170046
Shi, Liming ; Nielsen, Jesper K. ; Jensen, Jesper R. ; Little, Max A. ; Christensen, Mads G. / A Kalman-based fundamental frequency estimation algorithm. 2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2017. Vol. 2017-October IEEE, 2017. pp. 314-318
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Shi, L, Nielsen, JK, Jensen, JR, Little, MA & Christensen, MG 2017, A Kalman-based fundamental frequency estimation algorithm. in 2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2017. vol. 2017-October, IEEE, pp. 314-318, 2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2017, New Paltz, United States, 15/10/17. https://doi.org/10.1109/WASPAA.2017.8170046

A Kalman-based fundamental frequency estimation algorithm. / Shi, Liming; Nielsen, Jesper K.; Jensen, Jesper R.; Little, Max A.; Christensen, Mads G.

2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2017. Vol. 2017-October IEEE, 2017. p. 314-318.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Shi L, Nielsen JK, Jensen JR, Little MA, Christensen MG. A Kalman-based fundamental frequency estimation algorithm. In 2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2017. Vol. 2017-October. IEEE. 2017. p. 314-318 https://doi.org/10.1109/WASPAA.2017.8170046