Body sound denoising technologies: A survey and validation

Body sounds are closely related to physiological and mental health, offering valuable insights into both health monitoring and disease diagnosis. Recent studies have increasingly highlighted their potential in these tasks. However, the performance of these applications is significantly impacted by the inherent noise present in body sounds. This noise complicates diagnosis and monitoring, making effective denoising crucial. Effective noise removal from body sounds has been shown to enhance feature extraction and improve the performance of machine learning (ML) and/or deep learning (DL) models, thereby increasing the reliability of digital medical diagnostics. Despite the progress, there is currently no comprehensive summary of denoising techniques and their effectiveness. This survey aims to fill that gap by reviewing both classical and state-of-the-art denoising methods. We will first provide an overview of the theoretical foundations and recent advances in body sound denoising technologies. Next, we will detail the specific denoising techniques applied to different types of body sounds. Our analysis will include a comparison of various methods based on their performance using mainstream body sound databases. Finally, we validate and analyze some denoising algorithms, discuss current challenges and limitations, and suggest future research directions. This work promises to contribute to more accurate and effective body-sound based healthcare solutions.