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

• URL: http://arxiv.org/abs/2207.13843v1
• Date: Thu, 28 Jul 2022 01:05:40 GMT
• Title: Deep Learning-Based Acoustic Mosquito Detection in Noisy Conditions Using Trainable Kernels and Augmentations
• Authors: Devesh Khandelwal, Sean Campos, Shwetha Nagaraj, Fred Nugen, Alberto Todeschini
• Abstract summary: 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.
• Score: 17.77602155559703
• License: http://creativecommons.org/licenses/by/4.0/
• 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 Our results outperform the published baseline by 212% on the unpublished test set.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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