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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.","title":"PHALAR: Phasors for Learned Musical Audio Representations","url":"https://arxiv.org/abs/2605.03929","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.03929v2 Announce Type: cross \nAbstract: 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. 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