{"ID":2851986,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.18206","arxiv_id":"2510.18206","title":"Adaptive Per-Channel Energy Normalization Front-end for Robust Audio Signal Processing","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2510.18206","url_pdf":"https://arxiv.org/pdf/2510.18206v2","authors":"[\"Hanyu Meng\",\"Vidhyasaharan Sethu\",\"Eliathamby Ambikairajah\",\"Qiquan Zhang\",\"Haizhou Li\"]","published":"2025-10-21T01:20:59Z","proceeding":"eess.AS","tasks":"[\"eess.AS\",\"cs.SD\",\"eess.SP\"]","methods":"[]","has_code":false}