recording 03/04 — raw audio
Muud
A desktop app that hears what a song is — and how it feels.
instrument — audio in, feeling out: valence/arousal wander + genre memberships.
problem
Genre labels are flat. “Rock” tells you nothing about whether a track belongs at a funeral or a party — the feeling is the signal that matters for recommendation, and feeling is continuous, not categorical.
signal
Raw audio, decomposed twice. Mel spectrograms feed a 10-class CRNN trained on FMA-medium (temperature-scaled, multi-segment averaged, with hybrid labels when the top two genres land within 0.10). In parallel, a hybrid CNN plus handcrafted features — tempo, spectral centroid, RMS, zero-crossing rate — regresses continuous valence and arousal on the DEAM 1–9 scale.
build
The two heads meet in fuzzy fusion: 0.7 × graded genre similarity (an inter-genre matrix, so blues is near rock, not simply ≠) + 0.3 × emotion similarity, driving Spotify-backed recommendations. A live microphone mode runs a rolling spectrogram with inference every ~7 seconds and temporal smoothing across five windows. And because a model you can't interrogate is a mood ring, an explainability panel shows the actual fusion arithmetic, genre memberships, and a valence-arousal plot on Russell's circumplex.
proof
~68% validation accuracy on 10-way FMA genre classification — an honest number for a hard dataset — with both models trained from scratch, not rented from an API. Every recommendation can show its work.