Can Information Representations Inspired by the Human Auditory Perception Benefit Computer Audition-Based Disease Detection? An Interpretable Comparative Study

作者：Zhihua Wang, Haojie Zhang, Yang Tan, Rui Wang, Kun Qian, Bin Hu, Yoshiharu Yamamoto, Bj ̈orn W. Schuller · 发表于：IEEE Journal of Biomedical and Health Informatics · 年份：2025 · DOI：10.1109/jbhi.2025.3638846 · 被引用次数：5 · 研究领域：Phonocardiography and Auscultation Techniques、Music and Audio Processing、Voice and Speech Disorders

Computer audition-based methods have attracted a great deal of attention in the field of disease detection due to their significant advantages, e.g., non-invasive and convenient operation. Among them, the introduction of information representations inspired by human auditory perception, e.g., Mel-frequency transformation, gives it great potential to approach and even exceed the limits of the human auditory system. However, according to previous research, it remains challenging to fairly assess whether information representations inspired by human auditory perception have a significant positive effect on disease detection. Moreover, performance differences among various information representations and their underlying causes are yet to be thoroughly investigated and analyzed. To this end, we propose an interpretable comparative study on information representations inspired by human auditory perception for disease detection. First, the detection accuracy of different information representations are investigated on two sound datasets (a psychological and a physiological disease) based on the classical model and the proposed Temporal-Spatial Multi-Scale Perception Network. Then, the noise robustness of these information representations are compared by introducing Gaussian noise with varying signal-to-noise ratios (SNRs). Finally, by combining the human auditory perception mechanism and explainable AI techniques, we analyze the reasons for performance differences among various inf...