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arXiv · 2501.06146

xLSTM-SENet: xLSTM for Single-Channel Speech Enhancement

Nikolai Lund Kühne·Jan Østergaard·Jesper Jensen·Zheng-Hua Tan
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Abstract

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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Nikolai Lund Kühne, Jan Østergaard, Jesper Jensen, Zheng-Hua Tan. 2025-01-10. xLSTM-SENet: xLSTM for Single-Channel Speech Enhancement. https://arxiv.org/abs/2501.06146

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