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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.

Design Example

Design Example

The innovation of touch-tone telephony revolutionized the telecommunications industry by replacing the traditional rotary dial with a dual-tone multi-frequency (DTMF) signaling system. This system uses a matrix-style keypad with buttons arranged in four rows and three columns, creating 12 distinct signals each assigned to a pair of frequencies. Each button press results in a simultaneous generation of two sinusoidal tones – one from a low-frequency group (697 to 941 Hz) and one from a...

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...

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Related Experiment Video

Updated: Jul 24, 2025

Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages

Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages

Published on: March 24, 2023

Personalizing over-the-counter hearing aids using pairwise comparisons.

Dhruv Vyas¹, Ryan Brummet¹, Yumna Anwar¹

¹Department of Computer Science, University of Iowa, United States of America.

Smart Health (Amsterdam, Netherlands)

|July 3, 2023

Summary

This summary is machine-generated.

Over-the-counter hearing aids can now be personalized efficiently using a novel two-phase algorithm. This method uses user feedback effectively, reducing setup comparisons by half for better hearing accessibility.

Keywords:

Active learning Hearing aids Pairwise comparisons Personalization

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Related Experiment Videos

Last Updated: Jul 24, 2025

Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages

Systematic Hearing Performance Evaluation Process for Adolescents with Cochlear Implantation at Early Ages

Published on: March 24, 2023

A Two-interval Forced-choice Task for Multisensory Comparisons

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

Neuro-rehabilitation Approach for Sudden Sensorineural Hearing Loss

Neuro-rehabilitation Approach for Sudden Sensorineural Hearing Loss

Published on: January 25, 2016

Area of Science:

Audiology
Human-Computer Interaction
Machine Learning

Background:

Over-the-counter (OTC) hearing aids aim to increase accessibility and affordability of hearing healthcare.
A key challenge is enabling end-users to personalize device settings without professional audiologist assistance.
Efficient methods are needed to adapt numerous sound amplification parameters to individual user preferences.

Purpose of the Study:

To develop and evaluate a novel, efficient approach for personalizing OTC hearing aid configurations.
To reduce the number of user comparisons required for optimal hearing aid fitting.
To investigate user preference relationships within the hearing aid configuration space.

Main Methods:

Discretizing the 24-dimensional configuration space into a set of user-friendly presets.
Employing a multi-armed bandit (MAB) framework for an online agent to learn the best preset via pairwise comparisons.
Developing a Two-Phase Personalizing algorithm based on observed user preference patterns and Euclidean distance in the configuration space.

Main Results:

A user study with 35 participants was conducted to assess user preferences and MAB algorithm efficacy.
Identified a relationship between user preferences and presets, representable as preference points in the configuration space.
The proposed Two-Phase Personalizing algorithm significantly reduced comparison counts, achieving the best configuration with a median of 25 comparisons, halving the baseline requirement.

Conclusions:

It is feasible to configure OTC hearing aids effectively using minimal pairwise comparisons without professional intervention.
The novel approach enhances personalization and user experience for OTC hearing aid users.
This research contributes to making advanced hearing healthcare more accessible and user-driven.