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Auscultatory analysis using an electronic stethoscope has attractedincreasing attention in the clinical diagnosis of respiratory diseases.Recently, neural networks have been applied to assist in respiratory soundclassification with achievements. However, it remains challenging due to thescarcity of abnormal respiratory sound. In this paper, we propose a novelarchitecture, namely Waveform-Logmel audio neural networks (WLANN), which usesboth waveform and log-mel spectrogram as the input features and usesBidirectional Gated Recurrent Units (Bi-GRU) to context model the fusedfeatures. Experimental results of our WLANN applied to SPRSound respiratorydataset show that the proposed framework can effectively distinguishpathological respiratory sound classes, outperforming the previous studies,with 90.3% in sensitivity and 93.6% in total score. Our study demonstrates thehigh effectiveness of the WLANN in the diagnosis of respiratory diseases.

Waveform-Logmel Audio Neural Networks for Respiratory Sound Classification