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Semi-Automated Analysis of Peak Amplitude and Latency for Auditory Brainstem Response Waveforms Using R
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Wavelet speech enhancement algorithm using exponential semi-soft mask filtering.

Gihyoun Lee1, Sung Dae Na1, KiWoong Seong2

  • 1a Department of Medical & Biological Engineering, Graduate School , Kyungpook National University , Daegu , Korea.

Bioengineered
|July 21, 2016
PubMed
Summary

This study introduces an improved speech enhancement algorithm using wavelet packet decomposition and a novel semi-soft mask filter. The new method effectively minimizes signal loss and reduces residual noise for clearer speech.

Keywords:
binary mask filteringsemi-soft filteringspeech enhancementwavelet shrinkagewavelet transform

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Area of Science:

  • Signal Processing
  • Acoustics
  • Computer Science

Background:

  • Traditional mask filtering methods like the ideal binary mask (IBM) struggle to perfectly separate speech from noise.
  • This imperfect separation leads to residual noise and loss of desired speech components in conventional algorithms.

Purpose of the Study:

  • To develop a novel speech enhancement algorithm that overcomes the limitations of traditional mask filtering.
  • To minimize signal loss and effectively remove residual noise for improved speech clarity.

Main Methods:

  • The proposed algorithm utilizes wavelet packet decomposition for signal analysis.
  • A novel semi-soft mask filter with an exponential characteristic is employed to enhance speech components.
  • The semi-soft mask aims to reduce signal loss while the exponential filter targets residual noise reduction.

Main Results:

  • Experimental results demonstrate superior performance compared to traditional speech enhancement algorithms.
  • The proposed method effectively minimizes signal loss and reduces residual noise in various noisy conditions.
  • Objective and subjective evaluations confirm the enhanced quality of the processed speech signal.

Conclusions:

  • The developed speech enhancement algorithm offers a significant improvement over existing methods.
  • The combination of wavelet packet decomposition and the proposed semi-soft mask filtering effectively addresses residual noise and signal loss.
  • This approach holds promise for applications requiring high-quality speech processing in noisy environments.