MissawB/SonicMatch

Une plateforme interactive combinant le Machine Learning (XGBoost, Stacking) pour l'analyse de données Spotify et le traitement du signal (analyse spectrale) pour la reconnaissance audio de type Shazam. Inclus : moteur de recommandation, classification de genres et fingerprinting audio.

22
/ 100
Experimental
No Package No Dependents
Maintenance 13 / 25
Adoption 0 / 25
Maturity 9 / 25
Community 0 / 25

How are scores calculated?

Stars

—

Forks

—

Language

Jupyter Notebook

License

MIT

Category

spotify-music-recommendation

Last pushed

Mar 23, 2026

Commits (30d)

0

GitHub
Spotify Music Recommendation · 51 frameworks

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/MissawB/SonicMatch"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.

Higher-rated alternatives

ucalyptus/Spotify-Recommendation-Engine

Music Recommender System

43

ronibandini/reggaetonBeGone

Detects reggaeton genre with Machine Learning and sends packets to disable BT speakers (hopefully)

39

pooranjoyb/BeatBridge

A Music Player with a Clustering based Recommendation Engine utilizing Spotify API

37

mattmurray/music_recommender

Music recommender using deep learning with Keras and TensorFlow

35

maurocastermans/now-playing

Raspberry Pi application that detects music with ML, identifies it using Shazam, and shows the...

35

Explore ML Frameworks

All categories Trending ML Framework directory Insights