Machine Learning-Based Acoustic Signal Processing for Bowl Sound Analysis

Abstract

Acoustic data plays a pivotal role in scientific and engineering research across
various fields, including biology, communications, and Earth science. This study
investigates recent advancements in acoustics, specifically focusing on machine
learning (ML) and deep learning. ML, with its statistical techniques,
autonomously identifies patterns in data. Unlike traditional acoustics, ML
uncovers complex relationships among features and labels using extensive
training data. Applying ML to acoustic phenomena like human speech and
reverberation shows promising results. Additionally, this paper reviews acoustic
signal processing for bowel sound analysis, emphasizing noise reduction,
segmentation, feature extraction, and ML techniques. The integration of advanced
signal processing and ML holds significant potential.