May 26, 2025
Developing an Ear Training App for TET Identification
This document records the development of a tuning identification application created in collaboration with AI.
Using JavaScript, HTML, and CSS, a quiz-style ear training app was developed in which the user listens to a tone and guesses which equal temperament (TET: equal division of the octave) it belongs to.
The tones are played as sine waves.
Special care was taken to adjust the answer logic to handle cases where tones in different TETs (e.g., 12EDO and 24EDO) overlap.
Reiji's Observations
- I created my first music application with the help of AI, using JavaScript, HTML, and CSS.
- I handled the design specifications, sound testing, debugging, and gave instructions to the AI, which wrote the code.
- This is a quiz-style ear training app to guess the equal temperament (TET) of a played tone.
- During testing, I found edge cases like tones that belong to both 12-EDO and 24-EDO, so I adjusted the validation logic accordingly.
- The sounds are generated as sine waves, and the app calculates the corresponding TET division for each frequency to determine the answer.
Screenshot of the TET Ear Training App
The app is shown in "Custom Mode" (e.g., 9, 17, 19), where training is conducted across selected TETs.
| Output Link | Application HTML (tet_app.html) |
|---|---|
| Tuning Settings |
Identifies pitch classes across multiple TETs. Supports custom TET values between 1 and 99 (e.g., 12EDO, 24EDO, 53EDO). Sine wave tones are used for clarity. |
| Application Used |
HTML / JavaScript / CSS (developed in collaboration with AI) |
AI Assistant’s Notes and Inferences
- This application represents an advanced attempt to visualize and quantify pure musical perception.
- It holds potential as a training tool for honing pitch discrimination skills in multi-EDO environments.
- It also exemplifies ideal prototyping: rapidly going from idea to implementation through human-AI collaboration.