We present improvements to the refinement stage of YANGsaf[1] (Yet ANother Glottal source analysis framework), a recently published F0 estimation algorithm by Kawahara et al., for noisy/breathy speech signals. The baseline system, based on time-warping and weighted average of multi-band instantaneous frequency estimates, is still sensitive to additive noise when none of the harmonic provide reliable frequency estimate at low SNR. We alleviate this problem by calibrating the weighted averaging process based on statistics gathered from a Monte-Carlo simulation, and applying Kalman filtering to refined F0 trajectory with time-varying measurement and process distributions. The improved algorithm, adYANGsaf (adaptive Yet ANother Glottal source analysis framework), achieves significantly higher accuracy and smoother F0 trajectory on noisy speech while retaining its accuracy on clean speech, with little computational overhead introduced.
Cite as: Hua, K. (2017) Improving YANGsaf F0 Estimator with Adaptive Kalman Filter. Proc. Interspeech 2017, 2301-2305, doi: 10.21437/Interspeech.2017-21
@inproceedings{hua17_interspeech,
author={Kanru Hua},
title={{Improving YANGsaf F0 Estimator with Adaptive Kalman Filter}},
year=2017,
booktitle={Proc. Interspeech 2017},
pages={2301--2305},
doi={10.21437/Interspeech.2017-21}
}