  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on the unpublished test set.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.
