A Sparse Decomposition Method for Periodic Signal Mixtures
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Speech spectrum modeling for joint estimation of spectral envelope and fundamental frequency
IEEE Transactions on Audio, Speech, and Language Processing
A tandem algorithm for pitch estimation and voiced speech segregation
IEEE Transactions on Audio, Speech, and Language Processing
Low-complexity F0-based speech/nonspeech discrimination approach for digital hearing aids
Multimedia Tools and Applications
Multi-pitch Streaming of Harmonic Sound Mixtures
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
PEFAC - A Pitch Estimation Algorithm Robust to High Levels of Noise
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
| Hi-index | 0.00 |
This paper proposes a novel F0 contour estimation algorithm based on a precise parametric description of the voiced parts of speech derived from the power spectrum. The algorithm is able to perform in a wide variety of noisy environments as well as to estimate the F0s of cochannel concurrent speech. The speech spectrum is modeled as a sequence of spectral clusters governed by a common F0 contour expressed as a spline curve. These clusters are obtained by an unsupervised 2-D time-frequency clustering of the power density using a new formulation of the EM algorithm, and their common F 0 contour is estimated at the same time. A smooth F0 contour is extracted for the whole utterance, linking together its voiced parts. A noise model is used to cope with nonharmonic background noise, which would otherwise interfere with the clustering of the harmonic portions of speech. We evaluate our algorithm in comparison with existing methods on several tasks, and show 1) that it is competitive on clean single-speaker speech, 2) that it outperforms existing methods in the presence of noise, and 3) that it outperforms existing methods for the estimation of multiple F0 contours of cochannel concurrent speech