A Kalman-based Fundamental Frequency Estimation Algorithm

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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 as- sume that the fundamental frequency and amplitudes are station- ary 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 first- order 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 ampli- tude 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)
Number of pages5
Place of PublicationNew Paltz, NY, USA, USA
PublisherIEEE Press
Publication date15 Oct 2017
Pages314-318
ISBN (Print)978-1-5386-1633-8
ISBN (Electronic)978-1-5386-1632-1
DOIs
Publication statusPublished - 15 Oct 2017
Event2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics - Mohonk Mountain House, New Paltz, United States
Duration: 15 Oct 201718 Oct 2017
http://www.waspaa.com/

Workshop

Workshop2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics
LocationMohonk Mountain House
CountryUnited States
CityNew Paltz
Period15/10/201718/10/2017
Internet address
SeriesIEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)
ISSN1947-1629

Fingerprint

Frequency estimation
Extended Kalman filters
Kalman filters
Markov processes

Keywords

  • Fundamental frequency estimation, extended Kalman filter, 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) (pp. 314-318). New Paltz, NY, USA, USA: IEEE Press. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) https://doi.org/10.1109/WASPAA.2017.8170046
Shi, Liming ; Nielsen, Jesper Kjær ; Jensen, Jesper Rindom ; Little, Max A ; Christensen, Mads Græsbøll. / A Kalman-based Fundamental Frequency Estimation Algorithm. 2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA 2017). New Paltz, NY, USA, USA : IEEE Press, 2017. pp. 314-318 (IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)).
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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 as- sume that the fundamental frequency and amplitudes are station- ary 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 first- order 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 ampli- tude estimates for speech, the sustained vowel /a/ and solo musical tones with vibrato are demonstrated.",
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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). IEEE Press, New Paltz, NY, USA, USA, IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), pp. 314-318, 2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, New Paltz, United States, 15/10/2017. https://doi.org/10.1109/WASPAA.2017.8170046

A Kalman-based Fundamental Frequency Estimation Algorithm. / Shi, Liming; Nielsen, Jesper Kjær; Jensen, Jesper Rindom; Little, Max A; Christensen, Mads Græsbøll.

2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA 2017). New Paltz, NY, USA, USA : IEEE Press, 2017. p. 314-318 (IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)).

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

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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). New Paltz, NY, USA, USA: IEEE Press. 2017. p. 314-318. (IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)). https://doi.org/10.1109/WASPAA.2017.8170046