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English(EN) Listening Forward: Next Patch Embedding Prediction Enables Scalable Audio Learners

NAPE框架通过预测下一个补丁嵌入来推进音频表示学习

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员推出了一种新颖的音频自监督学习框架NAPE（Next-Audio-Patch-Embedding prediction）。该方法利用因果Transformer，通过预测对数梅尔频谱图的后续补丁嵌入来从先前的嵌入中学习，并将因果掩码和停止梯度作为其主要的训练信号。NAPE在六个音频和语音基准测试中取得了最先进的微调性能，展示了随着编码器尺寸的持续扩展和强大的线性探测结果。 AI

影响 NAPE在音频表示学习方面的成功可能会影响未来跨模态的自监督学习方法。

排序理由 该集群描述了一篇关于新颖音频处理自监督学习框架的最新研究论文。

在 Hugging Face Daily Papers 阅读 →

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NAPE框架通过预测下一个补丁嵌入来推进音频表示学习

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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Umberto Cappellazzo, Xubo Liu, Stavros Petridis, Maja Pantic ·

    向前聆听：下一个补丁嵌入预测实现可扩展音频学习器

    arXiv:2608.19863v1 Announce Type: cross Abstract: Self-supervised learning (SSL) has driven substantial progress in audio representation learning, though existing methods have increasingly relied on elaborate pre-training recipes to reach competitive performance. A markedly diffe…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    向前倾听：下一补丁嵌入预测实现可扩展音频学习器

    NAPE uses causal Transformers to predict successive spectrogram patch embeddings for self-supervised audio learning without auxiliary components.

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