Adaptive Per-Channel Energy Normalization Front-end for Robust Audio Signal Processing
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911404442779648 |
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| author | Meng, Hanyu Sethu, Vidhyasaharan Ambikairajah, Eliathamby Zhang, Qiquan Li, Haizhou |
| author_facet | Meng, Hanyu Sethu, Vidhyasaharan Ambikairajah, Eliathamby Zhang, Qiquan Li, Haizhou |
| contents | In audio signal processing, learnable front-ends have shown strong performance across diverse tasks by optimizing task-specific representation. However, their parameters remain fixed once trained, lacking flexibility during inference and limiting robustness under dynamic complex acoustic environments. In this paper, we introduce a novel adaptive paradigm for audio front-ends that replaces static parameterization with a closed-loop neural controller. Specifically, we simplify the learnable front-end LEAF architecture and integrate a neural controller for adaptive representation via dynamically tuning Per-Channel Energy Normalization. The neural controller leverages both the current and the buffered past subband energies to enable input-dependent adaptation during inference. Experimental results on multiple audio classification tasks demonstrate that the proposed adaptive front-end consistently outperforms prior fixed and learnable front-ends under both clean and complex acoustic conditions. These results highlight neural adaptability as a promising direction for the next generation of audio front-ends. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_18206 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adaptive Per-Channel Energy Normalization Front-end for Robust Audio Signal Processing Meng, Hanyu Sethu, Vidhyasaharan Ambikairajah, Eliathamby Zhang, Qiquan Li, Haizhou Audio and Speech Processing Sound Signal Processing In audio signal processing, learnable front-ends have shown strong performance across diverse tasks by optimizing task-specific representation. However, their parameters remain fixed once trained, lacking flexibility during inference and limiting robustness under dynamic complex acoustic environments. In this paper, we introduce a novel adaptive paradigm for audio front-ends that replaces static parameterization with a closed-loop neural controller. Specifically, we simplify the learnable front-end LEAF architecture and integrate a neural controller for adaptive representation via dynamically tuning Per-Channel Energy Normalization. The neural controller leverages both the current and the buffered past subband energies to enable input-dependent adaptation during inference. Experimental results on multiple audio classification tasks demonstrate that the proposed adaptive front-end consistently outperforms prior fixed and learnable front-ends under both clean and complex acoustic conditions. These results highlight neural adaptability as a promising direction for the next generation of audio front-ends. |
| title | Adaptive Per-Channel Energy Normalization Front-end for Robust Audio Signal Processing |
| topic | Audio and Speech Processing Sound Signal Processing |
| url | https://arxiv.org/abs/2510.18206 |