ArticlePLoS computational biology2023
Adaptive representations of sound for automatic insect recognition.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 23 citations in OpenAlex.
- Biodiversity isn't measured in tons: Making sense of nature in the context of sustainable agriculture.Ecological applications : a publication of the Ecological Society of America · 2026Review
- A frequency analysis of filterbank initialisation and noise augmentation for LEAF.Scientific reports · 2026Article
- A dataset of insect sounds from 459 species for bioacoustic machine learning.Scientific data · 2026Article
- Acoustic Detection of Insects in Stored Products in the Presence of Strong Ambient Noise.Sensors (Basel, Switzerland) · 2026Article
- Audio signal analysis using a modified forward-forward algorithm with enhanced segmentation for soil pest detection.Scientific reports · 2025Article
- Acoustic monitoring for tropical insect conservation.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Review
- Toward non-invasive early pest surveillance: cross-modal adaptation using PLMS acoustic-visual representation and pre-trained transfer learning.Frontiers in plant science · 2025Article
- Towards a toolkit for global insect biodiversity monitoring.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Article
- Extensive data engineering to the rescue: building a multi-species katydid detector from unbalanced, atypical training datasets.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Article
- Adaptive representations of sound for automatic insect recognition.PLoS computational biology · 2023Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Insect population numbers and biodiversity have been rapidly declining with time, and monitoring these trends has become increasingly important for conservation measures to be effectively implemented. But monitoring methods are often invasive, time and resource intense, and prone to various biases. Many insect species produce characteristic sounds that can easily be detected and recorded without large cost or effort. Using deep learning methods, insect sounds from field recordings could be automatically detected and classified to monitor biodiversity and species distribution ranges. We implement this using recently published datasets of insect sounds (up to 66 species of Orthoptera and Cicadidae) and machine learning methods and evaluate their potential for acoustic insect monitoring. We compare the performance of the conventional spectrogram-based audio representation against LEAF, a new adaptive and waveform-based frontend. LEAF achieved better classification performance than the mel-spectrogram frontend by adapting its feature extraction parameters during training. This result is encouraging for future implementations of deep learning technology for automatic insect sound recognition, especially as larger datasets become available.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.