A Kalman-based Fundamental Frequency Estimation Algorithm

Liming Shi; Jesper Kjær Nielsen; Jesper Rindom Jensen; Max A  Little; Mads Græsbøll Christensen

doi:10.1109/WASPAA.2017.8170046

A Kalman-based Fundamental Frequency Estimation Algorithm

Liming Shi, Jesper Kjær Nielsen, Jesper Rindom Jensen, Max A Little, Mads Græsbøll Christensen

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

13 Citations (Scopus)

110 Downloads (Pure)

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 language	English
Title of host publication	2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA 2017)
Number of pages	5
Place of Publication	New Paltz, NY, USA, USA
Publisher	IEEE Press
Publication date	15 Oct 2017
Pages	314-318
ISBN (Print)	978-1-5386-1633-8
ISBN (Electronic)	978-1-5386-1632-1
DOIs	https://doi.org/10.1109/WASPAA.2017.8170046
Publication status	Published - 15 Oct 2017
Event	2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics - Mohonk Mountain House, New Paltz, United States Duration: 15 Oct 2017 → 18 Oct 2017 http://www.waspaa.com/

Workshop

Workshop	2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics
Location	Mohonk Mountain House
Country/Territory	United States
City	New Paltz
Period	15/10/2017 → 18/10/2017
Internet address	http://www.waspaa.com/

Series	IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)
ISSN	1947-1629

Keywords

Fundamental frequency estimation, extended Kalman filter, harmonic model

Access to Document

10.1109/WASPAA.2017.8170046

preprintSubmitted manuscript, 1.07 MB

AUB Link

Search for the material in Aalborg University Library's search engine

Cite this

@inproceedings{db174639a7324ddd8c8edecd3ced8f8d,

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",

author = "Liming Shi and Nielsen, {Jesper Kj{\ae}r} and Jensen, {Jesper Rindom} and Little, {Max A} and Christensen, {Mads Gr{\ae}sb{\o}ll}",

year = "2017",

month = oct,

day = "15",

doi = "10.1109/WASPAA.2017.8170046",

language = "English",

isbn = "978-1-5386-1633-8",

series = "IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)",

publisher = "IEEE Press",

pages = "314--318",

booktitle = "2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA 2017)",

note = "2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2017 ; Conference date: 15-10-2017 Through 18-10-2017",

url = "http://www.waspaa.com/",

}

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, New York, 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 et al.
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 proceeding › Article in proceeding › Research › peer-review

TY - GEN

T1 - A Kalman-based Fundamental Frequency Estimation Algorithm

AU - Shi, Liming

AU - Nielsen, Jesper Kjær

AU - Jensen, Jesper Rindom

AU - Little, Max A

AU - Christensen, Mads Græsbøll

PY - 2017/10/15

Y1 - 2017/10/15

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.

KW - Fundamental frequency estimation, extended Kalman filter, harmonic model

U2 - 10.1109/WASPAA.2017.8170046

DO - 10.1109/WASPAA.2017.8170046

M3 - Article in proceeding

SN - 978-1-5386-1633-8

T3 - IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)

SP - 314

EP - 318

BT - 2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA 2017)

PB - IEEE Press

CY - New Paltz, NY, USA, USA

T2 - 2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics

Y2 - 15 October 2017 through 18 October 2017

ER -

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)). doi: 10.1109/WASPAA.2017.8170046

A Kalman-based Fundamental Frequency Estimation Algorithm

Abstract

Workshop

Keywords

Access to Document

AUB Link

Fingerprint

Cite this