PHALAR: Phasors for Learned Musical Audio Representations
Stem retrieval, the task of matching missing stems to a given audio submix, is a key challenge currently limited by models that discard temporal information. We introduce PHALAR, a contrastive framework achieving a relative accuracy increase of up to $\approx 70\%$ over the state-of-the-art while requiring $<50\%$ of the parameters and a 7$\times$ training speedup. By utilizing a Learned Spectral Pooling layer and a complex-valued head, PHALAR enforces pitch-equivariant and phase-equivariant biases. PHALAR establishes new retrieval state-of-the-art across MoisesDB, Slakh, and ChocoChorales, correlating significantly higher with human coherence judgment than semantic baselines. Finally, ze...
| Authors | Davide Marincione, Michele Mancusi, Giorgio Strano et al. |
|---|---|
| Primary category | cs.SD |
| Published | 5 May 2026 |
| Last updated at source | 26 May 2026 |
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