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 |
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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 | |
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 |
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Location | Mohonk Mountain House |
Country/Territory | United States |
City | New Paltz |
Period | 15/10/2017 → 18/10/2017 |
Internet address |
Series | IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) |
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ISSN | 1947-1629 |
Keywords
- Fundamental frequency estimation, extended Kalman filter, harmonic model