PEFAC - A Pitch Estimation Algorithm Robust to High Levels of Noise

@article{Gonzalez2014PEFACA,
  title={PEFAC - A Pitch Estimation Algorithm Robust to High Levels of Noise},
  author={Sira Gonzalez and Mike Brookes},
  journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
  year={2014},
  volume={22},
  pages={518-530}
}
  • S. GonzalezM. Brookes
  • Published 1 February 2014
  • Computer Science
  • IEEE/ACM Transactions on Audio, Speech, and Language Processing
We present PEFAC, a fundamental frequency estimation algorithm for speech that is able to identify voiced frames and estimate pitch reliably even at negative signal-to-noise ratios. The algorithm combines a normalization stage, to remove channel dependency and to attenuate strong noise components, with a harmonic summing filter applied in the log-frequency power spectral domain, the impulse response of which is chosen to sum the energy of the fundamental frequency harmonics while attenuating… 
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175 Citations
Highly Influential Citations
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Background Citations
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Methods Citations
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175 Citations

Voiced/Unvoiced Classification and Pitch Estimation Based on Amplitude Compression Filter

The simulated results show that the proposed method efficiently reduces voiced/unvoiced and pitch estimation error, and it is superior to some of the state-of-the-art method in the real environment.

A Pitch Estimation Method Robust to High Levels of Noise

By simulation experiments, it is shown that the proposed pitch detection method has more accurate and more low algorithm complexity than the traditional methods at both high and low SNR.

Unsupervised Classification of Voiced Speech and Pitch Tracking Using Forward-Backward Kalman Filtering

A new algorithm is presented that integrates the three subtasks of voiced speech, the estimation of the fundamental frequency and the tracking of pitch values over time into a single procedure and compares favorably with current, state-of-the-art pitch detection algorithms.

Fundamental Frequency Informed Speech Enhancement in a Flexible Statistical Framework

A statistical estimator is derived that explicitly takes into account information about the characteristic structure of voiced speech by means of a harmonic signal model and outperforms several reference algorithms in terms of speech quality and intelligibility as predicted by instrumental measures.

Combining Zero Replacement Speech Enhancement with Lag Window Method for Pitch Detection

An anti- noise pitch detection method that combines a speech enhancement algorithm with a spectral flattening algorithm is proposed that has the lowest gross pitch error (GPE) rate among all the methods when dealing with white-noise added male speeches.

Estimation of speaker individual spectral envelope for pitch tracking improvement

This paper proposes to overcome some limitations of the PEFAC algorithm by employing an alternative enhancement procedure, which uses an estimation of the individual spectral envelope instead of using a universal function.

Pitch Estimation Algorithm for Narrowband Speech Signal using Phase Differences between Harmonics

  • Yuya HosodaA. KawamuraY. Iiguni
  • Engineering
    2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)
  • 2021
This paper proposes a pitch estimation algorithm for a narrowband speech signal using phase differences between har-monics. A narrowband speech signal has an incomplete harmonic structure due to…

Robust Estimation of Fundamental Frequency Using Single Frequency Filtering Approach

A new method for robust estimation of fundamental frequency (F0) from speech signal is proposed in this paper. The method exploits the high SNR regions of speech in time and frequency domains in the…

Estimating acoustic speech features in low signal-to-noise ratios using a statistical framework

A Pitch-Synchronous Simultaneous Detection-Estimation Framework for Speech Enhancement

A pitch-synchronous stochastic-deterministic estimator outperforms several benchmark methods in terms of speech intelligibility and perceived quality predicted by instrumental measures for various noise types and different signal-to-noise ratios.
...

References

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