Spiking-LEAF: A Learnable Auditory front-end for Spiking Neural Networks
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866910378868342784 |
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| author | Song, Zeyang Wu, Jibin Zhang, Malu Shou, Mike Zheng Li, Haizhou |
| author_facet | Song, Zeyang Wu, Jibin Zhang, Malu Shou, Mike Zheng Li, Haizhou |
| contents | Brain-inspired spiking neural networks (SNNs) have demonstrated great potential for temporal signal processing. However, their performance in speech processing remains limited due to the lack of an effective auditory front-end. To address this limitation, we introduce Spiking-LEAF, a learnable auditory front-end meticulously designed for SNN-based speech processing. Spiking-LEAF combines a learnable filter bank with a novel two-compartment spiking neuron model called IHC-LIF. The IHC-LIF neurons draw inspiration from the structure of inner hair cells (IHC) and they leverage segregated dendritic and somatic compartments to effectively capture multi-scale temporal dynamics of speech signals. Additionally, the IHC-LIF neurons incorporate the lateral feedback mechanism along with spike regularization loss to enhance spike encoding efficiency. On keyword spotting and speaker identification tasks, the proposed Spiking-LEAF outperforms both SOTA spiking auditory front-ends and conventional real-valued acoustic features in terms of classification accuracy, noise robustness, and encoding efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_09469 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Spiking-LEAF: A Learnable Auditory front-end for Spiking Neural Networks Song, Zeyang Wu, Jibin Zhang, Malu Shou, Mike Zheng Li, Haizhou Sound Neural and Evolutionary Computing Audio and Speech Processing Brain-inspired spiking neural networks (SNNs) have demonstrated great potential for temporal signal processing. However, their performance in speech processing remains limited due to the lack of an effective auditory front-end. To address this limitation, we introduce Spiking-LEAF, a learnable auditory front-end meticulously designed for SNN-based speech processing. Spiking-LEAF combines a learnable filter bank with a novel two-compartment spiking neuron model called IHC-LIF. The IHC-LIF neurons draw inspiration from the structure of inner hair cells (IHC) and they leverage segregated dendritic and somatic compartments to effectively capture multi-scale temporal dynamics of speech signals. Additionally, the IHC-LIF neurons incorporate the lateral feedback mechanism along with spike regularization loss to enhance spike encoding efficiency. On keyword spotting and speaker identification tasks, the proposed Spiking-LEAF outperforms both SOTA spiking auditory front-ends and conventional real-valued acoustic features in terms of classification accuracy, noise robustness, and encoding efficiency. |
| title | Spiking-LEAF: A Learnable Auditory front-end for Spiking Neural Networks |
| topic | Sound Neural and Evolutionary Computing Audio and Speech Processing |
| url | https://arxiv.org/abs/2309.09469 |