| Services
our technique achieves near-clinical accuracy
This includes a self-test protocol available on the user’s mobile device (Android or iOS) and conducted via headphones.
We have developed a unique calibration methodology based on certified audiometry calibration equipment to enable this. This makes this test more accurate than any currently available self-test without specialized hardware.
The test results are used to design an audio filter that optimally compensates for the user’s hearing loss.
Our team is now developing a method to harness deep-learning algorithms to optimize hearing for speech understanding.
| Developed to answer the challenge
TUNEFORK IS NOT A SUBSTITUTE FOR HEARING AIDS. IT IS DESIGNED TO WORK IN PERFECT SYNCHRONIZATION WITH ALL BRANDS OF STANDARD HEARING AIDS.
TUNEFORK has developed an advanced personal Audio Profile system that characterizes an individual’s hearing. Our simple self-test is clinically accurate, delivers the best results, and is truly the Gold Standard in audio experience. The test results enable TUNEFORK to tailor a precise audio filter for every user, developing the perfect match between innovative mobile audio systems and hearing needs.
Tunefork can be integrated into any advanced mobile operating system to optimize all audio content:
Rings &
Alerts
AUDIO
ASSITANTS
RINGS &
ALERTS
VIDEO
GPS
PHONE CALLS
VOICE
MESSAGES
MUSIC
| TUNEFORK & Big Data
TUNEFORK is unique in developing an advanced database that holds all the data related to individual Audio Profiles and matches these profiles to technical data held on specific sound equipment, headphones, earbuds, and mobile devices. An Audio Profile can, therefore, be matched precisely to any known or registered piece of audio equipment.
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Competitive Advantages
Audio Profile; Earprint
(audio personalization)
Clinical accuracy
Perfect match to individual
audio needs
Improved Quality
of Life
Embedded Software
solution (no additional hardware needed)
Ease-of-use
Availability
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| TUNEFORK's Technology
This includes a self-test protocol based on the Hughson-Westlake technique, which achieves near-clinical accuracy in a subjectively quiet environment.
The test is available on the user’s mobile device (Android or iOS) and conducted via headphones. To enable this, we have developed a unique calibration methodology based on certified audiometry calibration equipment, making this test more accurate than any currently available self-test without specialized hardware.
The test results are used to design an audio filter that optimally compensates for the user’s hearing loss. Our team is also developing a method to harness deep-learning algorithms to optimize hearing for speech understanding.