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Hearing

Hearing

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.

Perceiving Loudness, Pitch, and Location

Perceiving Loudness, Pitch, and Location

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.
Place theory, or place coding, suggests that different pitches are heard because various sound waves activate specific locations along the cochlea's basilar membrane. The brain determines the pitch of a sound by identifying...

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Related Experiment Video

Updated: Jun 20, 2026

An Automated System for Sound Localization Testing in Hearing-Impaired Listeners

An Automated System for Sound Localization Testing in Hearing-Impaired Listeners

Published on: March 13, 2026

An algorithm that improves speech intelligibility in noise for normal-hearing listeners.

Gibak Kim¹, Yang Lu, Yi Hu

¹Department of Electrical Engineering, University of Texas at Dallas, Richardson, TX 75080, USA.

The Journal of the Acoustical Society of America

|September 11, 2009

Summary

This summary is machine-generated.

This study introduces a novel noise-suppression algorithm that significantly enhances speech intelligibility, even in noisy conditions. The algorithm improves speech clarity by selectively processing time-frequency units, outperforming traditional methods.

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Area of Science:

Speech processing
Auditory perception
Signal processing

Background:

Traditional noise-suppression algorithms improve speech quality but not intelligibility.
Previous studies explored intelligibility using the ideal binary mask for synthesized speech.

Purpose of the Study:

To propose and evaluate a novel algorithm for enhancing speech intelligibility.
To determine if time-frequency unit processing can improve speech perception in noise.

Main Methods:

An algorithm decomposes signals into time-frequency (T-F) units.
A Bayesian classifier determines if T-F units are dominated by target speech or masker noise.
Synthesized speech at low signal-to-noise ratios (SNRs) was presented to normal-hearing listeners.

Main Results:

The proposed algorithm demonstrated substantial improvements in speech intelligibility.
Intelligibility gains exceeded 60% points in -5 dB babble conditions compared to unprocessed speech.
Performance surpassed that of human listeners with unprocessed stimuli.

Conclusions:

Algorithms that reliably estimate the signal-to-noise ratio (SNR) in each T-F unit can significantly improve speech intelligibility.
This approach offers a promising direction for developing advanced speech enhancement technologies.
The findings have implications for hearing aid technology and assistive listening devices.