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Related Experiment Videos

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

Weijie Liu1,2, Yanyan Fan3

  • 1School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China.

Scientific Reports
|June 20, 2026
PubMed
Summary

This study introduces a novel framework for acoustic scene classification that enhances efficiency and cross-device performance. The method improves deep learning model interpretability by integrating physical audio properties, making it ideal for edge devices.

Keywords:
Acoustic scene classificationAsymmetric convolutionCross-device generalizationFeature disentanglementHarmonic-percussive source separation

Related Experiment Videos

Area of Science:

  • Audio signal processing
  • Machine learning
  • Deep learning for audio analysis

Background:

  • Acoustic scene classification (ASC) faces challenges in computational efficiency and cross-device generalization, particularly on resource-constrained devices.
  • Existing methods often struggle with robustness and adaptability in real-world, varied acoustic environments.

Purpose of the Study:

  • To propose a structure-aware dual-stream feature disentanglement framework for efficient and robust ASC.
  • To enhance the interpretability of deep learning models in audio processing by incorporating physical prior knowledge.

Main Methods:

  • Utilized harmonic-percussive source separation (HPSS) for structured acoustic signal decomposition.
  • Employed cascaded asymmetric convolution kernels for independent modeling of temporal-frequency characteristics.
  • Implemented a dual attention mechanism for adaptive feature fusion.

Main Results:

  • Significantly reduced parameter count and inference time compared to existing methods.
  • Achieved competitive classification accuracy with superior robustness in cross-device scenarios.
  • Demonstrated consistent generalization under reduced input durations and outperformed Transformer-based methods.

Conclusions:

  • The proposed framework offers a balance between performance and efficiency for ASC on edge and mobile devices.
  • The structure-aware feature disentanglement approach provides a novel perspective for enhancing deep learning model interpretability in audio analysis.