ML-ASPA: A Contemplation of Machine Learning-based Acoustic Signal Processing Analysis for Sounds, & Strains Emerging Technology

TL;DR

The paper addresses the challenge of extracting meaningful patterns from complex acoustic data across disciplines, including bowel sound analysis, where traditional signal processing can be complemented by data-driven ML models. It surveys ML fundamentals, signal identification and enhancement across time, frequency, and time–frequency domains, and advances in bowel-sound analysis, data acquisition, preprocessing, and feature extraction. It also details scalable computational strategies, presenting OpenMP and CUDA-based parallelizations and Barnes–Hut inspired methods to manage large model sets and pairwise computations. The work emphasizes the potential of open data and hybrid ML–physics approaches to advance acoustics research while acknowledging limitations around data requirements and interpretability, and it highlights practical pipelines for bowel-sound analysis and large-scale acoustic processing.

Abstract

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...

Figures (5)

• Figure 1: Time domain acoustic signal recorded from the gut
• Figure 2: Model generalization
• Figure 3: Model generalization
• Figure 4: Barnes-Hut tree structure
• Figure 5: Barnes-Hut domain decomposition