Compression of Respiratory Sound Signals via TranscoderQVAE

Respiratory diseases are one of the most common causes of death worldwide. At present, digital stethoscopes are valuable tools for diagnosing respiratory diseases, but they face limitations in storage, computation, and transmission capabilities. Therefore, developing respiratory sound compression algorithms is crucial. In this work, we propose TranscoderQVAE (TQVAE). TQVAE adopts the idea of variational autoencoder, non-uniform quantizer and Transformer to perform compression and recovery tasks on respiratory sounds Based on the concept of compressive sensing, we use an encoder to reduce data points and a nonuniform quantizer to compress the signal to 4 bits and stored which can reduce the storage space. Finally, we use a simplified Transformer as a decoder named Transcoder to recover signals. Meanwhile, a denoising algorithm based on wavelet transformation is applied to the recovered signal to remove noise. Through performing experiments of 3554 pieces of data, the correlation coefficient between the recovered signal and the raw signal can reach $\mathbf{0. 9 8}$. And the compression ratio can reach up to 256. Other evaluation metrics are also terrific. The algorithm execution time is less than 13 seconds per signal.