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.
|Title of host publication||2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2017|
|Number of pages||5|
|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||2017 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2017|
|Period||15/10/17 → 18/10/17|
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- extended Kalman filter
- Fundamental frequency estimation
- harmonic model