Scalable Animal Sound Detection: Hybrid Machine Learning Approaches for Real-World Bioacoustic Applications

Animal bioacoustics has emerged as an indispensable tool for biodiversity monitoring and ecosystem assessment, enabling non-invasive observation of wildlife populations across diverse habitats. Traditional acoustic classification systems employ handcrafted features such as Mel-Frequency Cepstral Coefficients (MFCCs) with classical machine learning classifiers, achieving reasonable performance in controlled environments but struggling with environmental noise, species vocalization variability, and cross-habitat generalization. This paper presents a hybrid classification framework that systematically compares classical and deep learning paradigms for animal sound recognition. A Random Forest classifier trained on 40-dimensional handcrafted acoustic features—encompassing spectral, temporal, and energy-based descriptors—establishes an interpretable baseline enabling feature importance analysis.