A Kalman-based Fundamental Frequency Estimation Algorithm

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Resumé

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.
OriginalsprogEngelsk
Titel2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA 2017)
Antal sider5
Udgivelses stedNew Paltz, NY, USA, USA
ForlagIEEE Press
Publikationsdato15 okt. 2017
Sider314-318
ISBN (Trykt)978-1-5386-1633-8
ISBN (Elektronisk)978-1-5386-1632-1
DOI
StatusUdgivet - 15 okt. 2017
Begivenhed2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics - Mohonk Mountain House, New Paltz, USA
Varighed: 15 okt. 201718 okt. 2017
http://www.waspaa.com/

Workshop

Workshop2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics
LokationMohonk Mountain House
LandUSA
ByNew Paltz
Periode15/10/201718/10/2017
Internetadresse
NavnIEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)
ISSN1947-1629

Fingerprint

Frequency estimation
Extended Kalman filters
Kalman filters
Markov processes

Citer dette

Shi, L., Nielsen, J. K., Jensen, J. R., Little, M. A., & Christensen, M. G. (2017). A Kalman-based Fundamental Frequency Estimation Algorithm. I 2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA 2017) (s. 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. s. 314-318 (IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)).
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title = "A Kalman-based Fundamental Frequency Estimation Algorithm",
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.",
keywords = "Fundamental frequency estimation, extended Kalman filter, harmonic model",
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Shi, L, Nielsen, JK, Jensen, JR, Little, MA & Christensen, MG 2017, A Kalman-based Fundamental Frequency Estimation Algorithm. i 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), s. 314-318, New Paltz, USA, 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. s. 314-318 (IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)).

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

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N2 - 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.

AB - 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. A Kalman-based Fundamental Frequency Estimation Algorithm. I 2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA 2017). New Paltz, NY, USA, USA: IEEE Press. 2017. s. 314-318. (IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)). https://doi.org/10.1109/WASPAA.2017.8170046