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Robust Harmonic Features for Classification-Based Pitch Estimation.

Dongmei Wang1, Chengzhu Yu1, John H L Hansen1

  • 1CRSS-CILab: Cochlear Implant Processing Lab, Center for Robust Speech Systems, University of Texas at Dallas, Richardson, TX 75080 USA.

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|April 6, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel pitch estimation algorithm using robust harmonic features and a neural network. The method accurately estimates pitch in noisy audio, outperforming existing techniques.

Keywords:
F0Fundamental frequencyneural networkpitch estimationrobust harmonic feature

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Area of Science:

  • Speech Processing
  • Spoken Language Technology
  • Audio Signal Analysis

Background:

  • Accurate pitch estimation in naturalistic audio is crucial for speech processing.
  • Existing methods face challenges with diverse audio streams and noise.

Purpose of the Study:

  • To develop a robust pitch estimation algorithm using harmonic features.
  • To improve pitch estimation accuracy in the presence of additive noise.

Main Methods:

  • A two-stage algorithm: pitch candidate generation and target pitch selection.
  • Utilized five robust harmonic features based on energy and spectral shape.
  • Employed a neural network for pitch salience modeling.
  • Applied Hidden Markov Models (HMM) and the Viterbi algorithm for pitch contour tracking.

Main Results:

  • The proposed algorithm demonstrated superior accuracy compared to state-of-the-art methods.
  • Effective pitch estimation was achieved in both high and low levels of additive noise.

Conclusions:

  • Robust harmonic features combined with neural networks offer a promising approach for accurate pitch estimation.
  • The algorithm shows significant potential for enhancing spoken language technologies in real-world conditions.