Songle

Problem

Music trivia games rely on playing full tracks, making it too easy to identify songs. There’s no game that challenges players with isolated stems — just vocals, drums, or bass — for a genuinely difficult guessing experience.

Solution

Built a daily Wordle-style music guessing game where players identify songs from isolated audio stems. A .NET API orchestrates Python ML models (Spleeter/Demucs) to separate uploaded tracks into vocals, drums, bass, and instrumentation.

Tradeoffs

Audio separation runs Python ML models invoked via subprocess from .NET rather than porting to native code. This adds deployment complexity (multi-container Docker setup) but leverages battle-tested models that would take months to reimplement.

Impact

Players get 4 attempts with scoring based on speed (100/75/50/25 points). A daily background job auto-selects the Song of the Day, and the hybrid .NET/Python pipeline processes tracks end-to-end from YouTube URL to separated stems stored in cloud storage.