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A corroborative study on improving pitch determination by time-frequency cepstrum decomposition using wavelets.

Fadoua Bahja1, Joseph Di Martino2, Elhassan Ibn Elhaj3

  • 1LRIT laboratory, Unit Associated to CNRST, URAC 29, Faculty of Sciences, Univrsité Mohammed V-Agdal, Avenue Ibn Batouta, B.P. 1014, Rabat, Morocco.

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Summary
This summary is machine-generated.

This study introduces a novel wavelet-based method for pitch period estimation and tracking. The approach enhances pitch detection accuracy using discrete wavelet transform and dual tree complex wavelet transform on cepstrum excitation signals.

Keywords:
Approximation coefficientsCepstrum signalPitch estimationPitch trackingVoicing decisionWavelet transforms

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Area of Science:

  • Signal Processing
  • Acoustics
  • Speech Technology

Background:

  • Accurate pitch period estimation is crucial for speech analysis and synthesis.
  • Existing pitch detection algorithms face challenges in real-time processing and accuracy, especially with complex vocal signals.

Purpose of the Study:

  • To develop and evaluate a novel wavelet-based method for improved pitch period estimation and tracking.
  • To enhance the accuracy and efficiency of pitch detection using advanced wavelet transforms.

Main Methods:

  • Extraction of the cepstrum excitation signal.
  • Application of discrete wavelet transform (DWT) and dual tree complex wavelet transform (DTCWT) for signal decomposition.
  • Smoothing of approximation coefficients and implementation of an efficient voicing decision criterion.

Main Results:

  • Experimental results demonstrate significant performance improvements in pitch detection compared to previous algorithms.
  • The proposed methods show high accuracy on standard speech databases (Bagshaw and Keele) with both male and female speakers.
  • The voicing decision criterion ensures low latency and reduced classification errors, suitable for real-time applications.

Conclusions:

  • Wavelet transform-based decomposition of the cepstrum excitation signal effectively improves pitch detection.
  • The novel approach offers superior performance and efficiency for pitch tracking, outperforming existing methods.
  • The method is robust and accurate for diverse speaker populations and speech conditions.