Deep Learning-Based Acoustic Mosquito Detection in Noisy Conditions Using Trainable Kernels and Augmentations
Sound
2022-08-22 v2 Machine Learning
Audio and Speech Processing
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 Our results outperform the published baseline by 212% on the unpublished test set.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.
Cite
@article{arxiv.2207.13843,
title = {Deep Learning-Based Acoustic Mosquito Detection in Noisy Conditions Using Trainable Kernels and Augmentations},
author = {Devesh Khandelwal and Sean Campos and Shwetha Nagaraj and Fred Nugen and Alberto Todeschini},
journal= {arXiv preprint arXiv:2207.13843},
year = {2022}
}