1. Deep Learning-Based Acoustic Mosquito Detection in Noisy Conditions Using Trainable Kernels and Augmentations
- Author
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Khandelwal, Devesh, Campos, Sean, Nagaraj, Shwetha, Nugen, Fred, and Todeschini, Alberto
- Subjects
Computer Science - Sound ,Computer Science - Machine Learning ,Electrical Engineering and Systems Science - Audio and Speech Processing ,I.2.m ,I.2.1 - 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 Our results outperform the published baseline by 212% on the unpublished test set.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.
- Published
- 2022
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