Business Overview
2016
$1.4M
Seed
3
2
TuneFork is an audio personalization technology software that delivers an optimal hearing experience, improving the quality of life for people with hearing loss and seniors.Hearing loss affects more than 400 million people around the world, Most seniors (more than 1 billion worldwide) generally suffer from some degree of hearing loss, making their everyday lives challenging. Many of them experience difficulty using their smart devices such as smartphones, smart homes, smart assistants, smart automotive and more, all of which rely on clarity of sound for efficient operation.TuneFork enables the user to perform a simple hearing test and grades the user’s specific hearing problem. The test results, paired with sophisticated algorithms to adjust frequencies, create an optimal, custom-made audio experience for the user a personalized Audio Profile.TuneFork can be integrated into any operating system and can optimize all audio content: phone calls, rings and alerts, TV shows, voice messages, music, video, GPS instructions, in-car infotainment, smart assistant response and much more.
Founders (2)
| Name | Title | Contact | |
|---|---|---|---|
 | ㅤYoav Blau | CTO | |
 | ㅤTomer Shor | CEO | 1 |
Board and Advisors (0)
Funding Rounds (3)
| Round | Date Announced | Investors | |
|---|---|---|---|
 | ㅤTunefork raises $1.5 million seed | June 4, 2020 | 1 |
| ㅤTunefork raises $30,000 award | September 12, 2019 | 1 |
| ㅤTunefork raises $58,000 grant | December 1, 2018 | 1 |
M&A Events (0)
Investors (2)
| Investor Name | Investor Type | AUM ($) | |
|---|---|---|---|
| ㅤHorizon 2020 | 0 | ||
 | ㅤTriventures | Venture Capital | 130,000,000 |
Patents (0)
Research Publications (0)
Certifications (0)
AI Technology Stack
Tunefork is unique in its development of an advanced data base which not only holds all the data related to individual Audio Profiles, but it can match these profiles to technical data held on specific sound equipment, headphones, ear-buds and mobile devices. An Audio Profile can therefore be matched precisely to any known ore registered piece of audio equipment. Our team is now developing a method to harness deep-learning algorithms to optimize hearing for speech understanding.
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Jobs (0)