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arXiv Machine Learning By Haider Al-Tahan, Sean O'Brien, Anastasia Razdaibiedina, N. Apurva Ratan Murty

LAST: Looped Audio Spectrogram Transformer

Read the original on arXiv Machine Learning →

The paper introduces LAST, a Looped Audio Spectrogram Transformer that processes all tokens once and then reuses the same blocks to refine only the class token over fixed audio features, making subsequent passes inexpensive. On AudioSet, a ten‑pass LAST outperforms a twelve‑layer sequential transformer by 2.1% relative mean average precision while using 49.4% fewer parameters, 42% fewer MACs, and achieving 9.8% higher throughput. Increasing the pass count from two to ten improves accuracy with only a 1.2% increase in computation, and the model shows enhanced robustness to temporal masking and other auditory augmentations across music, environmental, and event sound classification tasks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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