Convolutional Auto-Encoder for Variable Length Respiratory Sound Compression and Reconstruction

This paper presents a respiratory sound compression and reconstruction method based on convolutional Auto-Encoder. By utilizing convolutional and transpose convolutional layers, this model can process variable length sound waveform, which is an important feature for data transmission from edge-based medical devices to cloud server and reconstruct the signal with high fidelity. This work shows that utilizing a non-variational latent space in respiratory sounds compression generates smaller reconstruction error compared to other state-of-art solution. Additionally, this work proposes a new composite loss function to guide the network training. Tested with BioCAS 2024 Grand Challenge dataset, this method achieves a Percent Root Mean Square Difference (PRD) of 0.2230, Correlation Coefficient (CC) of 0.972, and Signal-to-Noise Ratio Loss (SNRL) ${- 0. 7 1 2 9} \mathrm{d B}$ with an average compression rate of 222.