Deep Learning-Based Acoustic Mosquito Detection in Noisy Conditions Using Trainable Kernels and Augmentations

In this paper, we demonstrate a unique recipe to enhance the effectiveness of audio machine learning approaches [3] by fusing pre-processing techniques into a deep learning model [12]. 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'22 challenge [20]. Our results outperform the published baseline by 212% on the unpublished test set.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.