A Kalman-based fundamental frequency estimation algorithm


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.

Publication DOI: https://doi.org/10.1109/WASPAA.2017.8170046
Divisions: College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Funding Information: ∗This work was funded by the Danish Council for Independent Research, grant ID: DFF 4184-00056.
Additional Information: © 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.
Event Title: 2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2017
Event Type: Other
Event Dates: 2017-10-15 - 2017-10-18
Uncontrolled Keywords: extended Kalman filter,Fundamental frequency estimation,harmonic model,Electrical and Electronic Engineering,Computer Science Applications
ISBN: 9781538616321
Last Modified: 03 Jun 2024 08:01
Date Deposited: 08 Mar 2018 09:50
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Conference contribution
Published Date: 2017-12-11
Accepted Date: 2017-12-10
Authors: Shi, Liming
Nielsen, Jesper K.
Jensen, Jesper R.
Little, Max A. (ORCID Profile 0000-0002-1507-3822)
Christensen, Mads G.



Version: Accepted Version

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