Music Classification by Spectrogram: Self-Supervised Audio Embedding Pipeline
Self-Supervised Audio Embedding Pipeline
¶ 01Context & Design Rationale
“A reliable system is designed not around hypothetical optimal conditions, but around deterministic resilience under resource pressure.”
Self-supervised contrastive learning system that trains a CNN encoder on augmented mel-spectrograms to produce audio embeddings without requiring labeled data. The learned representations support music genre classification, song recommendation, and audio similarity search. Preprocessing converts raw audio to both standard and Mel spectrograms; the encoder and projection head are trained with a SimCLR-style objective on augmented views of each track. Engineered with strict architectural boundaries, deterministic memory models, and zero extraneous runtime dependencies.
¶ 02Architectural Pipeline
SUBSYSTEM RUNTIMEINGESTION PHASE
Protocol Parser
Zero-Copy Frame
→
TRANSFORMATION
Core Logic Unit
Deterministic Arena
→
PERSISTENCE
Storage / State Layer
Atomic Append