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arXiv · 2605.03929

PHALAR: Phasors for Learned Musical Audio Representations

Davide Marincione·Michele Mancusi·Giorgio Strano·Luca Cerovaz·Donato Crisostomi·Roberto Ribuoli·Emanuele Rodolà
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Abstract

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, zero-shot beat tracking and linear chord probing confirm that PHALAR captures robust musical structures beyond the retrieval task.

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Davide Marincione, Michele Mancusi, Giorgio Strano, Luca Cerovaz, Donato Crisostomi, Roberto Ribuoli, Emanuele Rodolà. 2026-05-26. PHALAR: Phasors for Learned Musical Audio Representations. https://arxiv.org/abs/2605.03929

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