Acoustic Analysis

Acoustic analysis focuses on extracting meaningful information from sound, aiming to understand its physical properties, sources, and implications across diverse fields. Current research emphasizes the application of machine learning, particularly neural networks (including recurrent, transformer, and physics-informed architectures), and advanced statistical models (like Kalman filters and QGAMMs) to analyze acoustic data for various purposes, such as disease diagnosis, sound source localization, and speech enhancement. This work has significant implications for improving healthcare diagnostics (e.g., Parkinson's disease detection), optimizing resource allocation (e.g., chest x-ray usage), and enhancing technological applications (e.g., virtual environment sound design and VoIP quality).