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<p>Addressing the computational efficiency and cross-device generalization challenges faced by acoustic scene classification in resource-constrained environments, this study proposes a structure-aware dual-stream feature disentanglement framework based on harmonic-percussive source separation and asymmetric convolutions. The framework achieves structured decomposition of acoustic signals through HPSS techniques, performs independent modeling targeting temporal-frequency dimension differentiated characteristics through cascaded asymmetric convolution kernels, and realizes adaptive feature fusion through a dual attention mechanism. Systematic validation on the DCASE 2020 and TAU Urban Acoustic Scenes 2022 datasets demonstrates that the proposed method significantly reduces parameter count and inference time while maintaining competitive classification accuracy, exhibiting superior robustness compared to Transformer-based methods in cross-device scenarios and consistent generalization capability under reduced input duration conditions. This study provides a solution balancing performance and efficiency for acoustic scene classification deployment on edge devices and mobile platforms, with the proposed structure-aware feature disentanglement concept offering a new perspective for enhancing the interpretability of deep learning models through leveraging physical prior knowledge in the audio signal processing domain.</p>

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Structure-aware acoustic scene classification: a feature decoupling framework using HPSS and asymmetric convolutions

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月20日

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  • Weijie Liu,
  • Yanyan Fan

摘要

Addressing the computational efficiency and cross-device generalization challenges faced by acoustic scene classification in resource-constrained environments, this study proposes a structure-aware dual-stream feature disentanglement framework based on harmonic-percussive source separation and asymmetric convolutions. The framework achieves structured decomposition of acoustic signals through HPSS techniques, performs independent modeling targeting temporal-frequency dimension differentiated characteristics through cascaded asymmetric convolution kernels, and realizes adaptive feature fusion through a dual attention mechanism. Systematic validation on the DCASE 2020 and TAU Urban Acoustic Scenes 2022 datasets demonstrates that the proposed method significantly reduces parameter count and inference time while maintaining competitive classification accuracy, exhibiting superior robustness compared to Transformer-based methods in cross-device scenarios and consistent generalization capability under reduced input duration conditions. This study provides a solution balancing performance and efficiency for acoustic scene classification deployment on edge devices and mobile platforms, with the proposed structure-aware feature disentanglement concept offering a new perspective for enhancing the interpretability of deep learning models through leveraging physical prior knowledge in the audio signal processing domain.