| --- |
| license: cc-by-nc-sa-4.0 |
| pipeline_tag: audio-classification |
| tags: |
| - autrainer |
| - audio |
| - ecoacoustic-tagging |
| - HearTheSpecies |
| --- |
| |
| # InsectNet |
| This model was developed in the course of our research paper "InsectNet: A Machine-Learning Classifier for Insect Stridulations of Europe" which will be published soon. |
| The model tags audio files as belonging to one or more of 80 prevalent Orthoptera and Hemiptera species in Europe. |
|
|
| # Installation |
|
|
| To use the model, you have to install autrainer, e.g. via pip: |
|
|
| ``` |
| pip install autrainer |
| ``` |
|
|
| This model has been trained and tested with autrainer version `0.6.0`. |
| For more information about autrainer, please refer to: https://autrainer.github.io/autrainer/index.html |
|
|
| # Usage |
|
|
| The model can be applied on all wav files present in a folder (`<data-root>`) and stored in another folder (`<output-root>`): |
|
|
| ``` |
| autrainer inference hf:HearTheSpecies/InsectNet -r <data-root> <output-root> -w 4 -s 4 -sr 96000 |
| ``` |
| , where `-w` is the window size in seconds, `-s` is the step size in seconds and `-sr` is the sampling rate. |
| For other possible inference settings and all usable parameters, please have a look at the autrainer documentation. |
| However, the above settings are recommended. |
|
|
| **Caution!** Specifying the sampling rate as 96000 is necessary in order to ensure proper windowed inference, as our model was trained with this sampling rate. |
|
|
| ## Training |
|
|
| ### Pretraining |
|
|
| We used the CNN10 architecture, a pretrained audio neural network (PANN), introduced by Kong et al. (2020), which was pretrained on AudioSet. |
|
|
| ### Dataset |
|
|
| The training data comprised the following data sources: |
|
|
| Strong labels: |
| - ECOSoundSet (Funosas et al., 2026): Orthoptera and Hemiptera species as well as buzz clips. |
| - Additional annotations by the University of Freiburg and Simon Thorne: Orthoptera species. |
| - WABAD (Pérez-Granados et al., 2026): for bird recordings. |
| - Edansa-2019 (Çoban, E. B., et al., 2022): for silence recordings |
|
|
| Weak labels: |
| - InsectSet459 (Faiß et al., 2026): Orthoptera species |
| - Additional xeno-canto recordings which were not included in InsectSet459: Orthoptera species |
|
|
| The comprised classes are listed within `target_transform.yaml` as labels. |
|
|
| ### Features |
|
|
| The audio recordings were resampled to 96kHz, as we wanted to avoid losing too much frequency information from the species. |
| Log-Mel spectrograms were then extracted using torchlibrosa. |
|
|
| ### Training process |
|
|
| The model has been trained for 30 epochs. At the end of each epoch, the model was evaluated on our validation set. |
| We release the state that achieved the best performance on this validation set. |
| All training hyperparameters can be found inside `conf/config.yaml` inside the model folder. |
| The input audios to the model were 4s long. If necessary shorter/longer audio files have been padded/cropped. |
|
|
|
|
| ## Evaluation |
|
|
| For the evaluation we only consider the results based on the Orthoptera and Hemiptera (OH) species, i.e., the results on silence, buzz, and birds are not considered. |
| We do this, as these classes were merely introduced to learn a more robust feature exctrator, but not in order to classify them later on. The focus lies on the OH species. |
| The performance on the test set, when using the default threshold of 0.5, reached a (macro) **F1 score of .670**. |
|
|
|
|
| ## Acknowledgments |
|
|
| Please acknowledge the work which produced the original model. We would appreciate an acknowledgment to autrainer. |
|
|
| ## Bibliography |
| - Rampp, S., et al. (2024). Autrainer: A modular and extensible deep learning toolkit for computer audition tasks. arXiv preprint arXiv:2412.11943. |
| - Kong, Q., et al. (2020). Panns: Large-scale pretrained audio neural networks for audio pattern recognition. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 28, 2880-2894. |
| - Funosas, D., et al. (2026). A finely annotated dataset for the automated acoustic identification of European Orthoptera and Cicadidae. Scientific Data. |
| - Pérez‐Granados, C., et al. (2026). WABAD: A world annotated bird acoustic dataset for passive acoustic monitoring. Ecology, 107(2), e70317. |
| - Faiß, M., et al. (2026). A dataset of insect sounds from 459 species for bioacoustic machine learning. Scientific Data. |
| - Çoban, E. B., et al. (2022). Edansa-2019: The ecoacoustic dataset from arctic north slope alaska. In Workshop on the detection and classification of acoustic scenes and events. |
