ML-ASPA: A Contemplation of Machine Learning-based Acoustic Signal
  Processing Analysis for Sounds, & Strains Emerging Technology
Ratul Ali Aktarul Islam Md. Shohel Rana Saila Nasrin Sohel Afzal Shajol Professor Dr. A. H. M. Saifullah Sadi
Acoustic data serves as a fundamental cornerstone in advancing scientific and
engineering understanding across diverse disciplines, spanning biology,
communications, and ocean and Earth science. This inquiry meticulously explores
recent advancements and transformative potential within the domain of
acoustics, specifically focusing on machine learning (ML) and deep learning.
ML, comprising an extensive array of statistical techniques, proves
indispensable for autonomously discerning and leveraging patterns within data.
In contrast to traditional acoustics and signal processing, ML adopts a
data-driven approach, unveiling intricate relationships between features and
desired labels or actions, as well as among features themselves, given ample
training data. The application of ML to expansive sets of training data
facilitates the discovery of models elucidating complex acoustic phenomena such
as human speech and reverberation. The dynamic evolution of ML in acoustics
yields compelling results and holds substantial promise for the future. The
advent of electronic stethoscopes and analogous recording and data logging
devices has expanded the application of acoustic signal processing concepts to
the analysis of bowel sounds. This paper critically reviews existing literature
on acoustic signal processing for bowel sound analysis, outlining fundamental
approaches and applicable machine learning principles. It chronicles historical
progress in signal processing techniques that have facilitated the extraction
of valuable information from bowel sounds, emphasizing advancements in noise
reduction, segmentation, signal enhancement, feature extraction, sound
localization, and machine learning techniques...