Structural Bottlenecks on Frequency Representation in End-to-End Audio Models
End-to-end neural audio models achieve high-fidelity compression and generation. We might read that performance as evidence they directly represent interpretable features such as pitch and timbre, but a model can produce plausible outputs without doing so. A model may encode these features in any reachable basis, but regardless of which, the features are well described as compositions of time-frequency-localized primitives. Whether state-of-the-art encoders preserve access to these primitives, and thus to compositions of them, remains unclear. Through theoretical analysis and controlled experiments, we show that several state-of-the-art strided convolutional encoders impose two structural...
| Authors | Nicole Cosme-Clifford |
|---|---|
| Primary category | cs.SD |
| Published | 9 July 2026 |
| Last updated at source | 9 July 2026 |
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