首个基于xLSTM的单通道语音增强模型，性能超越主流架构。
xLSTM-SENet: xLSTM for Single-Channel Speech Enhancement
- 用xLSTM替代注意力机制，实现线性扩展性
- 在VoiceBank+Demand上优于同等复杂度的Mamba与Conformer
- 指数门控和双向结构是关键设计亮点
尽管基于注意力的架构（如Conformer）在语音增强中表现优异，但其计算复杂度随输入序列长度呈非线性增长。相比之下，新型扩展长短期记忆网络（xLSTM）具有线性可扩展性。本文首次提出基于xLSTM的单通道语音增强系统xLSTM-SENet。在VoiceBank+Demand数据集上的对比实验表明，xLSTM甚至普通LSTM均可在不同模型规模下达到或超过当前最优的Mamba与Conformer系统。消融实验揭示指数门控与双向结构是其有效性的关键因素。最佳模型xLSTM-SENet2在相似复杂度下超越现有最先进Mamba与Conformer系统。
原文摘要 · Abstract (English)
While attention-based architectures, such as Conformers, excel in speech enhancement, they face challenges such as scalability with respect to input sequence length. In contrast, the recently proposed Extended Long Short-Term Memory (xLSTM) architecture offers linear scalability. However, xLSTM-based models remain unexplored for speech enhancement. This paper introduces xLSTM-SENet, the first xLSTM-based single-channel speech enhancement system. A comparative analysis reveals that xLSTM-and notably, even LSTM-can match or outperform state-of-the-art Mamba- and Conformer-based systems across various model sizes in speech enhancement on the VoiceBank+Demand dataset. Through ablation studies, we identify key architectural design choices such as exponential gating and bidirectionality contributing to its effectiveness. Our best xLSTM-based model, xLSTM-SENet2, outperforms state-of-the-art Mamba- and Conformer-based systems of similar complexity on the Voicebank+DEMAND dataset.
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