SInES Tools: Genre Recognizer
(AI based genre classification with Essentia.js, Discogs-EffNet and MTG-Jamendo-Genre Model)

1. Click on " Start" to load the AI Model.
2. Select the desired classification model:
3. Upload one or multiple mp3 or wav audio file(s).
4. optional: If you have uploaded multiple audio files to analyze them in a batch:
   Additionally, export a table for each file containing all individual values over time as a .zip file
5. Click on " Analyse" to start the detection process.
6. optional (Single-File only): View the classified features in the plot. (Note: Only the Top 7 genres are visible by default.)
7. Click on "Show JS Arrays" or "Export CSV", to display or export the recorded data.

Click "Start" to load the Models.

 Analysis Parameters: Window Size: STFT: 32 ms, 128 Frames: 2048 ms | Hop Size: 16 ms, 128 Frames: 2048 ms |
FFT Size: 512 Samples (96 Mel Bands) | Sample Rate (internal): 16000 Hz
SInES Tools: Genre Recognizer © 2026 by Christoph Reuter and SInES (last modified: 13.92026) is licensed under Affero GPLv3 (AGPLv3).
Please note: Essentia and the underlying Essentia library are licensed under the Affero GPLv3 (AGPLv3).
This library is available under the AGPLv3 for non-commercial use.
Translation of the Chinese version of the website: Li Hao
How to cite: Reuter, C. (2026). SInES Tools: Genre Recognizer. Web Application https://sinestools.univie.ac.at/genre400detector.htm