A Kalman-based fundamental frequency estimation algorithm

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

doi:10.1109/WASPAA.2017.8170046

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/Published conference output › Conference publication

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 language	English
Title of host publication	2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2017
Publisher	IEEE
Pages	314-318
Number of pages	5
Volume	2017-October
ISBN (Electronic)	9781538616321
DOIs	https://doi.org/10.1109/WASPAA.2017.8170046
Publication status	Published - 11 Dec 2017
Event	2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2017 - New Paltz, United States Duration: 15 Oct 2017 → 18 Oct 2017

Conference

Conference	2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2017
Country/Territory	United States
City	New Paltz
Period	15/10/17 → 18/10/17

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

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10.1109/WASPAA.2017.8170046

A KALMAN-BASED FUNDAMENTAL FREQUENCY ESTIMATION ALGORITHM
© 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.
Accepted author manuscript, 854 KB

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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 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.",

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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

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AU - Christensen, Mads G.

N1 - © 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.

PY - 2017/12/11

Y1 - 2017/12/11

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 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.

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 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.

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A Kalman-based fundamental frequency estimation algorithm

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Keywords

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