LAST: Looped Audio Spectrogram Transformer
Increasing depth of transformer models improves recognition, but it comes at a substantial cost. Each additional layer requires more parameters, which makes the process computationally inefficient. We ask whether additional processing can focus on integrating features already computed. Looped Audio Spectrogram Transformer (LAST) first processes all tokens, then reuses the same blocks to refine only the class token over fixed audio features, thereby making later passes inexpensive. On AudioSet, ten-pass LAST achieves 0.345 mean average precision, exceeding a twelve-layer sequential transformer by 2.1% relative with 49.4% fewer parameters, 42% fewer multiply-accumulate operations, and 9.8% ...
| Authors | Haider Al-Tahan, Sean O'Brien, Anastasia Razdaibiedina et al. |
|---|---|
| Primary category | cs.SD |
| Published | 1 October 2026 |
| Last updated at source | 1 October 2026 |
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