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The performance of an automatic acoustic-based program classifier compared to hearing aid users' manual selection of
Grant D Searchfield1,2, Tania Linford1,2, Kei Kobayashi2
1a Section of Audiology , The University of Auckland , Auckland , New Zealand.
International Journal of Audiology
|October 27, 2017
Summary
Automatic sound classification in hearing aids offers a viable alternative to manual program selection. This study found automatic systems performed better for speech in various noise conditions, matching manual selection for sound quality.
Area of Science:
- Audiology
- Hearing Aid Technology
- Acoustics
Background:
- Manual selection of hearing aid programs can be complex for users.
- Automatic sound classification aims to simplify hearing aid operation by adapting to different listening environments.
Purpose of the Study:
- To compare user preference and objective performance between manually selected hearing aid programs and an automatic sound classifier (Phonak AutoSense OS).
Main Methods:
- A single-blind, repeated-measures study involved 25 participants with moderate-severe sensorineural hearing loss.
- Participants used Phonak Virto V90 ITE hearing aids, comparing manual program preferences against the AutoSense OS in four sound scenarios.
- Performance was evaluated using the Hearing in Noise Test (HINT) and sound quality ratings after a 4-week trial.
Main Results:
- User preferences for manual programs varied significantly.
- The automatic classifier demonstrated a Speech Reception Threshold (SRT) advantage over manual selection in quiet, loud noise, and car noise scenarios.
- Subjective sound quality ratings were comparable between manual and automatic program selections.
Conclusions:
- Automatic sound classification presents a practical alternative to manual program selection for hearing aid users.
- The Phonak AutoSense OS shows potential for improving speech intelligibility in challenging listening environments.
