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arXiv · 2507.02666

ASDA: Audio Spectrogram Differential Attention Mechanism for Self-Supervised Representation Learning

Junyu Wang·Tianrui Wang·Meng Ge·Longbiao Wang·Jianwu Dang
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Abstract

In recent advancements in audio self-supervised representation learning, the standard Transformer architecture has emerged as the predominant approach, yet its attention mechanism often allocates a portion of attention weights to irrelevant information, potentially impairing the model's discriminative ability. To address this, we introduce a differential attention mechanism, which effectively mitigates ineffective attention allocation through the integration of dual-softmax operations and appropriately tuned differential coefficients. Experimental results demonstrate that our ASDA model achieves state-of-the-art (SOTA) performance across multiple benchmarks, including audio classification (49.0% mAP on AS-2M, 41.5% mAP on AS20K), keyword spotting (98.3% accuracy on SPC-2), and environmental sound classification (96.1% accuracy on ESC-50). These results highlight ASDA's effectiveness in audio tasks, paving the way for broader applications.

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Junyu Wang, Tianrui Wang, Meng Ge, Longbiao Wang, Jianwu Dang. 2025-07-03. ASDA: Audio Spectrogram Differential Attention Mechanism for Self-Supervised Representation Learning. https://arxiv.org/abs/2507.02666

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