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arXiv · 2207.13843

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

Devesh Khandelwal·Sean Campos·Shwetha Nagaraj·Fred Nugen·Alberto Todeschini
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Abstract

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 Our results outperform the published baseline by 212% on the unpublished test set.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.

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Devesh Khandelwal, Sean Campos, Shwetha Nagaraj, Fred Nugen, Alberto Todeschini. 2022-07-28. Deep Learning-Based Acoustic Mosquito Detection in Noisy Conditions Using Trainable Kernels and Augmentations. https://doi.org/10.1145/3503161.3551586

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