Automatic synthesis of insects bioacoustics using machine learning : a systematic review.

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dc.contributor.authorKyalo, Henry
dc.contributor.authorTonnang, Henri
dc.contributor.authorEgonyu, James
dc.contributor.authorOlukuru, John
dc.contributor.authorTanga, Chrysantus
dc.contributor.authorSenagi, Kennedy
dc.date.accessioned2026-07-03T10:00:01Z
dc.date.available2026-07-03T10:00:01Z
dc.date.issued2025
dc.descriptionResearch article
dc.description.abstractThe application of machine learning has received increasing attention in the synthesis of insect sounds to preserve biodiversity. This study reviewed current literature on the application of these techniques in the automatic synthesis of insect bioacoustic and their applications in insects as food and feed, improving pest management, and as well as managing pollinators. To achieve this, the study used Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to identify, screen, and include the final articles used in this review, based on criteria such as papers addressing machine learning in insect acoustics, biodiversity, ecology conservation, etc. This study revealed that most of the researchers used secondary data and the microphone was the common tool used to record sound signals. Sound signals were mainly pre-processed using techniques such as denoising, segmentation, and windowing. Sound signal classification algorithms were categorized mainly as shallow and deep machine learning algorithms. In the shallow machine learning algorithms, the most common method of feature extraction was the Mel-Frequency Cepstral Coefficient (MFCC) and the Support Vector Machine (SVM) was the most commonly used algorithm. In deep learning, spectrogram image features were widely extracted and the Convolutional Neural Network (CNN) was mostly used to synthesize the spectral features. This paper also reviewed recent developments in insect bioacoustics signals processing, applications, and future directions. Generally, machine learning algorithms can be applied and deployed successfully to different insects’ automatic synthesis problems to improve the production of insects (as food and/or feed), and improve/preserve diversity and life on Earth. Keywords Insects · Bioacoustics · Machine learning · Insects as food · Insects as feed · Ecological conservation
dc.description.sponsorshipKenya Education Network Trust (KENET), grant number AGMT1170 ; Australian Centre for International Agricultural Research (ACIAR) (ProteinAfrica: LS/2020/154) ; Novo Nordisk Foundation (RefIPro:NNF22SA0078466) ; Busitema University
dc.identifier.citationKyalo, H., Tonnang, H., Egonyu, J., Olukuru, J., Tanga, C., & Senagi, K. (2025). Automatic synthesis of insects bioacoustics using machine learning: a systematic review. International Journal of Tropical Insect Science, 45(1), 101–120. https://doi.org/10.1007/s42690-024-01406-2
dc.identifier.urihttps://bdears.busitema.ac.ug/handle/123456789/7347
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofseriesInternational Journal of Tropical Insect Science ; (2025) 45:101–120
dc.titleAutomatic synthesis of insects bioacoustics using machine learning : a systematic review.
dc.typeArticle
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