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An Interpretable Four-Branch Wavelet Attention Network for Pathological Voice Detection
编号：192 访问权限：仅限参会人 更新：2026-10-19 18:11:44 浏览：6次 张贴报告

报告开始：暂无开始时间（Asia/Shanghai）

报告时间：暂无持续时间

所在会场：[暂无会议] [暂无会议段]

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摘要
Abstract—Automatic detection of pathological voice can provide objective assistance for laryngeal disease screening, treatment follow-up, and telemedicine. Traditional methods typically rely on handcrafted acoustic features such as fundamental frequency, jitter, shimmer, harmonic-to-noise ratio, and mel-frequency cepstral coefficients, and their performance is susceptible to feature selection, recording conditions, and pathology variations. Deep learning can learn discriminative features directly from raw waveforms or time-frequency representations, but its internal decision process lacks clear interpretability. To address this issue, this paper proposes Four-Branch Wavelet Attention Network (FWANet). Parallel branches are constructed using db16, sym8, coif8, and rbio3.1 wavelets. Each branch alternates six stages of one-level discrete wavelet transform and one-dimensional convolution, while frequency-channel attention is applied in the last five stages to adaptively recalibrate low- and high-frequency features. After temporal alignment and sample-level branch weighting, a Cross-Wavelet Temporal Feature Fusion (CTFF) module integrates local temporal variation and cross-wavelet channel information. FWANet achieves accuracies of 0.9800, 0.7734, and 0.8545 on the MEEI, SVD, and HUPA databases, respectively, with MCC values of 0.9522, 0.5024, and 0.7153. Ablation results show that CTFF consistently improves Accuracy and MCC over the corresponding four-branch variant without CTFF. Branch weights and layer-wise frequency-channel attention further provide hierarchical interpretability.
 
关键词
pathological voice classification; discrete wavelet transform; multilayer wavelet attention; frequency-channel attention; interpretable deep learning
报告人
Le Jiang
student Soochow University

稿件作者
Le Jiang Soochow University
Fuming Ji Soochow University
Yiting Zhang Soochow University
MingXuan Yan Soochow University
xiaojun Zhang Soochow University
Zhi Tao Soochow University
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重要日期

  • 会议日期

    11月06日

    2026

    至

    11月08日

    2026

  • 10月15日 2026

    初稿截稿日期

主办单位
IEEE Instrumentation and Measurement Society
承办单位
Sichuan University
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