Turning music identification into a neural forward pass
Search, a foundational operation in computer science, maps a query to a matching item in a collection. It is typically implemented as a System-2 like, rule-based pipeline in which a key is computed, an index is probed, and candidates are verified. By contrast, human recognition resembles a System-1 like, associative model of identity recovery, in which even partial cues can trigger a recall without explicitly enumerating, ranking, or even accessing discrete candidates. Here, we show that music sound identification, a difficult search problem, can be performed in a single neural feed-forward pass by a generative transformer. Trained on an audio dataset, the model predicts the corresponding...
| Authors | Muhammad Taimoor Haseeb, Ahmad Hammoudeh, Gus Xia |
|---|---|
| Primary category | cs.SD |
| Published | 15 June 2026 |
| Last updated at source | 15 June 2026 |
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