A Kalman-based Fundamental Frequency Estimation Algorithm

Research output: Research - peer-reviewArticle in proceeding

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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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
Publication date27 Jun 2017
StateAccepted/In press - 27 Jun 2017
Publication categoryResearch
Peer-reviewedYes
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
LandUnited States
ByNew Paltz
Periode15/10/201718/10/2017
Internetadresse

    Research areas

  • Fundamental frequency estimation, extended Kalman filter, harmonic model

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ID: 260111039