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arXiv · 2408.16568

AxLSTMs: learning self-supervised audio representations with xLSTMs

Sarthak Yadav·Sergios Theodoridis·Zheng-Hua Tan
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Abstract

While the transformer has emerged as the eminent neural architecture, several independent lines of research have emerged to address its limitations. Recurrent neural approaches have observed a lot of renewed interest, including the extended long short-term memory (xLSTM) architecture, which reinvigorates the original LSTM. However, while xLSTMs have shown competitive performance compared to the transformer, their viability for learning self-supervised general-purpose audio representations has not been evaluated. This work proposes Audio xLSTM (AxLSTM), an approach for learning audio representations from masked spectrogram patches in a self-supervised setting. Pretrained on the AudioSet dataset, the proposed AxLSTM models outperform comparable self-supervised audio spectrogram transformer (SSAST) baselines by up to 25% in relative performance across a set of ten diverse downstream tasks while having up to 45% fewer parameters.

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Sarthak Yadav, Sergios Theodoridis, Zheng-Hua Tan. 2024-08-29. AxLSTMs: learning self-supervised audio representations with xLSTMs. https://doi.org/10.21437/interspeech.2025-426

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