Applying Fourier Neural Operator to insect wingbeat sound classification: Introducing CF-ResNet-1D
Mosquitoes and other insects are vectors of severe diseases, posing significant health risks to millions worldwide yearly. Effective classification of insect species, particularly through their wingbeat sounds, is crucial for disease prevention and control. Despite recent advancements in Deep Learni...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-05-01
|
| Series: | Ecological Informatics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1574954125000640 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1825199398040109056 |
|---|---|
| author | Béla J. Szekeres Máté Natabara Gyöngyössy János Botzheim |
| author_facet | Béla J. Szekeres Máté Natabara Gyöngyössy János Botzheim |
| author_sort | Béla J. Szekeres |
| collection | DOAJ |
| description | Mosquitoes and other insects are vectors of severe diseases, posing significant health risks to millions worldwide yearly. Effective classification of insect species, particularly through their wingbeat sounds, is crucial for disease prevention and control. Despite recent advancements in Deep Learning, Fourier Neural Operators (FNO), efficient for solving Partial Differential Equations due to their global spectral representations, have yet to be thoroughly explored for real-world time series classification or regression tasks. This study explores the application of FNOs in insect wingbeat sound classification, focusing on their potential for improving the accuracy and efficiency of such tasks, particularly in the fight against mosquito-borne diseases. We introduce CF-ResNet-1D, a novel ResNet-inspired model that integrates Convolutional Fourier Layers, combining the strengths of FNOs and 1D-Convolutional processing. The model is designed to analyze raw time-domain signals, leveraging the parallel spectral processing capabilities of FNOs. Our findings demonstrate that CF-ResNet-1D significantly outperforms traditional spectrogram-based models in classifying insect wingbeat sounds, achieving state-of-the-art accuracy. |
| format | Article |
| id | doaj-art-21078b0a9f9943fe93a1cfe44f83fb12 |
| institution | Kabale University |
| issn | 1574-9541 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Informatics |
| spelling | doaj-art-21078b0a9f9943fe93a1cfe44f83fb122025-02-08T05:00:01ZengElsevierEcological Informatics1574-95412025-05-0186103055Applying Fourier Neural Operator to insect wingbeat sound classification: Introducing CF-ResNet-1DBéla J. Szekeres0Máté Natabara Gyöngyössy1János Botzheim2Department of Numerical Analysis, Faculty of Informatics, ELTE, Pázmány P. stny. 1/C, Budapest, 1117, Hungary; Corresponding author.Department of Artificial Intelligence, Faculty of Informatics, ELTE, Pázmány P. stny. 1/A, Budapest, 1117, HungaryDepartment of Artificial Intelligence, Faculty of Informatics, ELTE, Pázmány P. stny. 1/A, Budapest, 1117, HungaryMosquitoes and other insects are vectors of severe diseases, posing significant health risks to millions worldwide yearly. Effective classification of insect species, particularly through their wingbeat sounds, is crucial for disease prevention and control. Despite recent advancements in Deep Learning, Fourier Neural Operators (FNO), efficient for solving Partial Differential Equations due to their global spectral representations, have yet to be thoroughly explored for real-world time series classification or regression tasks. This study explores the application of FNOs in insect wingbeat sound classification, focusing on their potential for improving the accuracy and efficiency of such tasks, particularly in the fight against mosquito-borne diseases. We introduce CF-ResNet-1D, a novel ResNet-inspired model that integrates Convolutional Fourier Layers, combining the strengths of FNOs and 1D-Convolutional processing. The model is designed to analyze raw time-domain signals, leveraging the parallel spectral processing capabilities of FNOs. Our findings demonstrate that CF-ResNet-1D significantly outperforms traditional spectrogram-based models in classifying insect wingbeat sounds, achieving state-of-the-art accuracy.http://www.sciencedirect.com/science/article/pii/S1574954125000640Audio classificationResNet architectureDeep learningMosquito wingbeatFourier Neural Operator |
| spellingShingle | Béla J. Szekeres Máté Natabara Gyöngyössy János Botzheim Applying Fourier Neural Operator to insect wingbeat sound classification: Introducing CF-ResNet-1D Ecological Informatics Audio classification ResNet architecture Deep learning Mosquito wingbeat Fourier Neural Operator |
| title | Applying Fourier Neural Operator to insect wingbeat sound classification: Introducing CF-ResNet-1D |
| title_full | Applying Fourier Neural Operator to insect wingbeat sound classification: Introducing CF-ResNet-1D |
| title_fullStr | Applying Fourier Neural Operator to insect wingbeat sound classification: Introducing CF-ResNet-1D |
| title_full_unstemmed | Applying Fourier Neural Operator to insect wingbeat sound classification: Introducing CF-ResNet-1D |
| title_short | Applying Fourier Neural Operator to insect wingbeat sound classification: Introducing CF-ResNet-1D |
| title_sort | applying fourier neural operator to insect wingbeat sound classification introducing cf resnet 1d |
| topic | Audio classification ResNet architecture Deep learning Mosquito wingbeat Fourier Neural Operator |
| url | http://www.sciencedirect.com/science/article/pii/S1574954125000640 |
| work_keys_str_mv | AT belajszekeres applyingfourierneuraloperatortoinsectwingbeatsoundclassificationintroducingcfresnet1d AT matenatabaragyongyossy applyingfourierneuraloperatortoinsectwingbeatsoundclassificationintroducingcfresnet1d AT janosbotzheim applyingfourierneuraloperatortoinsectwingbeatsoundclassificationintroducingcfresnet1d |