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Related Concept Videos

Hearing01:31

Hearing

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When we hear a sound, our nervous system is detecting sound waves—pressure waves of mechanical energy traveling through a medium. The frequency of the wave is perceived as pitch, while the amplitude is perceived as loudness.
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The human brain perceives pitch through two primary mechanisms reflected in place theory and frequency theory. Each mechanism describes how sound waves are interpreted as specific pitches by the brain, offering insights into the intricate processes of auditory perception.
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Related Experiment Video

Updated: Aug 27, 2025

Sound Source Localization Testing in Single-sided Deafness Following Bone Conduction Intervention
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Spatial speech detection for binaural hearing aids using deep phoneme classifiers.

Hendrik Kayser1,2, Hynek Hermansky3, Bernd T Meyer4,2

  • 1Auditory Signal Processing & Hearing Devices, Carl von Ossietzky University, 26111 Oldenburg, Germany.

Acta Acustica. European Acoustics Association
|September 26, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for hearing aids to spatially detect speech using sound source localization and optimize speech enhancement. Two automatic speech recognition-based quality measures reliably identify speech targets in various noise conditions.

Keywords:
Automatic speech recognitionDeep neural networkDirection-of-arrival estimation

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Area of Science:

  • Audiology and Speech Processing
  • Signal Processing
  • Artificial Intelligence in Hearing Aids

Background:

  • Current hearing aids struggle with speech-specific optimization for spatial sound sources.
  • Effective speech enhancement in noisy environments remains a challenge for binaural hearing aid users.

Purpose of the Study:

  • To develop and evaluate a novel approach for spatial speech detection and blind speech enhancement in binaural hearing aids.
  • To combine direction of arrival (DOA) estimation with automatic speech recognition-based speech quality measures for improved hearing aid performance.

Main Methods:

  • Integrated a high-resolution DOA estimator with a low-resolution speech quality measure based on phoneme representations from a deep neural network (DNN).
  • Explored three automatic speech quality measures (ASQM): entropy, mean temporal distance (M-Measure), and matched phoneme (MaP) filtering.
  • Tested the approach in diverse acoustic scenes with varying signal-to-noise ratios (SNR), noise types (localized/diffuse), and reverberation levels.

Main Results:

  • Two ASQMs, M-Measure and MaP filtering, reliably identified the speech target across different acoustic conditions.
  • The proposed system demonstrated robust performance without environment adaptation or prior scene information.
  • The approach successfully detected incorrect spatial filtering angles, indicating its effectiveness in real-world scenarios.

Conclusions:

  • The developed approach enhances spatial speech detection and blind speech enhancement for binaural hearing aids.
  • ASQMs based on M-Measure and MaP filtering show significant promise for improving hearing aid functionality in complex acoustic environments.
  • The system's adaptability and lack of reliance on a priori information make it a practical solution for advanced hearing aid technology.