Emanuele Rodolà, Michele Mancusi, Donato Crisostomi, Giorgio Strano, Davide Marincione, Luca Cerovaz, Roberto Ribuoli
We lifted 3 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| gladia-research-group/phalar | canonical | 2 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| complex_normalize | Ran | gladia-research-group/phalar/contrastive_model/similarity_ops.py code served (permissive licence) · get_code("aa89ee116e16d0f3") |
| make_pre_pool_projector | Ran | gladia-research-group/phalar/contrastive_model/contrastive_model.py code served (permissive licence) · get_code("cbf9325555fcb0fa") |
| get_normalization_factor | Not yet run | gladia-research-group/phalar/wrappers/phalar_downstream_wrapper.py code served (permissive licence) · get_code("852aebd5b555be31") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
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 ≈ 70% over the state-of-the-art while requiring < 50% of the parameters and a 7× 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 stateof-the-art across MoisesDB, Slakh, and Choco-Chorales, 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.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2605.03929")
get_code_for_paper("2605.03929")
have("2605.03929")
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