Artificial Intelligence Enabled Self‐Powered Acoustics Sensing: From Energy Harvesting to Healthcare Monitoring

Abstract Acoustic signals are vital as they contain key information in characteristic parameters, including phase, amplitude, frequency, harmonics, signal‐to‐noise ratio, waveform, spectral density, reverberation, and modulation patterns. Recent advancements in signal processing and machine learning have significantly transformed the application of acoustic signals, from early detection of vocal cord diseases to enhancing biometric technology through voiceprint biometrics. Self‐powered acoustic sensors enable non‐invasive continuous monitoring, which aids in the recognition of various pulmonary and cardiovascular diseases such as asthma, bronchitis, aortic stenosis, and congestive heart failure. Beyond healthcare, it invaluably contributed to environmental monitoring, industrial diagnostics, and structural health monitoring, detecting and analyzing sounds from natural events, machinery, and infrastructure for early anomaly detection and proactive intervention. This article provides a comprehensive overview of acoustic sensing and energy harvesting methodologies, key parameters, and various machine learning algorithms. Use cases are discussed to demonstrate the potential of AI‐integrated acoustic sensors across different domains, along with strategies to mitigate errors in developing these systems.