Deep Learning-Based Acoustic Mosquito Detection in Noisy Conditions Using Trainable Kernels and Augmentations
This work provides a robust bio-acoustic system for mosquito detection, which is an incremental advancement in domain-specific applications.
The paper tackles the problem of detecting mosquitoes from audio signals in noisy conditions by fusing pre-processing techniques into a deep learning model, resulting in a 212% improvement over the baseline on an unpublished test set.
In this paper, we demonstrate a unique recipe to enhance the effectiveness of audio machine learning approaches by fusing pre-processing techniques into a deep learning model. Our solution accelerates training and inference performance by optimizing hyper-parameters through training instead of costly random searches to build a reliable mosquito detector from audio signals. The experiments and the results presented here are part of the MOS C submission of the ACM 2022 challenge. Our results outperform the published baseline by 212% on the unpublished test set. We believe that this is one of the best real-world examples of building a robust bio-acoustic system that provides reliable mosquito detection in noisy conditions.