From croaks to species: A review of advances in automated acoustic analysis for anurans

Acoustic communication is widespread across animal taxa, from insects to whales, with many species including Anurans using sequences of distinct sound elements for interaction. Trends in the Anurans population provide crucial information on ecosystem health, pollution levels, habitat destruction, and impacts of climate change. Amphibians face a significant risk of extinction, with two out of every five species currently threatened. Bioacoustics, the study of sound production, transmission, and reception in animals, plays a vital role in understanding Anuran behavior, monitoring biodiversity, conservation, and advancing ecological research. With the proliferation of Passive Acoustic Monitoring (PAM) systems, there has been rapid progress in acoustic preprocessing such as signal denoising, syllable segmentation, feature extraction, and classification techniques. However, the literature remains fragmented across computational and ecological disciplines, hindering the synthesis of technological advancements and their applications. This review provides an overview of recent developments in acoustic preprocessing and machine learning for automatic anuran acoustic analysis. It aims to serve as a comprehensive resource for ecologists and computational scientists by highlighting prevalent methods, emerging trends, commonly used datasets, and research gaps within the bioacoustic pipeline. Ultimately, this review supports climate action and terrestrial biodiversity conservation (SDG 13 and SDG 15) by synthesizing advances in anuran bioacoustic research toward robust, scalable, and climate-sensitive biodiversity monitoring systems. • Summarising recent advances in acoustic preprocessing, feature extraction, and classification for bioacoustic analysis. • Reviewing the role of machine learning, including deep learning, transfer and few-shot learning, in handling limited bioacoustic data. • Comparing existing tools, datasets, and methodologies across computational and ecological studies. • Identifying major research gaps, including the lack of standardised and large-scale anuran datasets. • Recommending future directions for developing robust and scalable anuran acoustic monitoring systems.