Noise-Resilient Bioacoustics Feature Extraction Methods and Their Implications on Audio Classification Performance: Systematic Review

作者：Geofrey Owino, Bernard Shibwabo · 发表于：JMIR Biomedical Engineering · 年份：2025 · DOI：10.2196/80089 · 被引用次数：4 · 研究领域：Medicine

Abstract Background Bioacoustics classification plays a crucial role in ecological surveillance and neonatal health monitoring. Infant cry analysis can aid early health diagnostics, while ecological acoustics informs conservation. However, the presence of environmental noise, signal variability, and limited annotated datasets often hinders model reliability and deployment. Robust feature extraction and denoising techniques have become critical for improving model robustness, enabling more accurate interpretation of acoustic events across diverse bioacoustic domains under real-world conditions. Objective This review systematically evaluates advancements in noise-resilient feature extraction and denoising techniques for bioacoustics classification. Specifically, it explores methodological trends, model types, cross-domain transferability between clinical and ecological applications, and evidence for real-world deployment. Methods A systematic review was conducted by searching 8 electronic databases, including IEEE Xplore, ScienceDirect, Web of Science, ACM Digital Library, and Scopus, through December 2024. Eligible studies entailed audio-based classification models and applied empirical or computational evaluations of bioacoustics classification using machine learning or deep learning methods. In addition, studies also included explicit or implicit consideration of noise. Two reviewers independently screened studies, extracted data, and assessed quality. Risk of bias was asses...