Acoustic Identification of Ae. aegypti Mosquitoes using Smartphone Apps and Residual Convolutional Neural Networks

• URL: http://arxiv.org/abs/2306.10091v1
• Date: Fri, 16 Jun 2023 13:41:01 GMT
• Title: Acoustic Identification of Ae. aegypti Mosquitoes using Smartphone Apps and Residual Convolutional Neural Networks
• Authors: Kayu\~a Oleques Paim and Ricardo Rohweder and Mariana Recamonde-Mendoza and Rodrigo Brand\~ao Mansilha1 and Weverton Cordeiro
• Abstract summary: We advocate for smartphone apps as low-cost, easy-to-deploy solution for raising awareness among the population on the proliferation of Aedes aegypti mosquitoes. devising such a smartphone app is challenging, for many reasons, including the required maturity level of techniques for identifying mosquitoes based on features that can be captured using smartphone resources.
• Score: 0.8808021343665321
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we advocate in favor of smartphone apps as low-cost, easy-to-deploy solution for raising awareness among the population on the proliferation of Aedes aegypti mosquitoes. Nevertheless, devising such a smartphone app is challenging, for many reasons, including the required maturity level of techniques for identifying mosquitoes based on features that can be captured using smartphone resources. In this paper, we identify a set of (non-exhaustive) requirements that smartphone apps must meet to become an effective tooling in the fight against Ae. aegypti, and advance the state-of-the-art with (i) a residual convolutional neural network for classifying Ae. aegypti mosquitoes from their wingbeat sound, (ii) a methodology for reducing the influence of background noise in the classification process, and (iii) a dataset for benchmarking solutions for detecting Ae. aegypti mosquitoes from wingbeat sound recordings. From the analysis of accuracy and recall, we provide evidence that convolutional neural networks have potential as a cornerstone for tracking mosquito apps for smartphones.

Related papers

• Automatic AI Model Selection for Wireless Systems: Online Learning via Digital Twinning [50.332027356848094]
  AI-based applications are deployed at intelligent controllers to carry out functionalities like scheduling or power control. The mapping between context and AI model parameters is ideally done in a zero-shot fashion. This paper introduces a general methodology for the online optimization of AMS mappings.
  arXiv  Detail & Related papers  (2024-06-22T11:17:50Z)
• MosquIoT: A System Based on IoT and Machine Learning for the Monitoring of Aedes aegypti (Diptera: Culicidae) [0.0]
  This paper presents the design, development, and testing of an innovative system named MosquIoT. Based on traditional ovitraps with embedded Internet of Things (IoT) and Tiny Machine Learning (TinyML) technologies, it enables the detection and quantification of Ae. aegypti eggs.
  arXiv  Detail & Related papers  (2024-01-29T16:08:18Z)
• Convolutional Monge Mapping Normalization for learning on sleep data [63.22081662149488]
  We propose a new method called Convolutional Monge Mapping Normalization (CMMN) CMMN consists in filtering the signals in order to adapt their power spectrum density (PSD) to a Wasserstein barycenter estimated on training data. Numerical experiments on sleep EEG data show that CMMN leads to significant and consistent performance gains independent from the neural network architecture.
  arXiv  Detail & Related papers  (2023-05-30T08:24:01Z)
• mAedesID: Android Application for Aedes Mosquito Species Identification using Convolutional Neural Network [0.0]
  It is important to control dengue disease by reducing the spread of Aedes mosquito vectors. Community awareness plays acrucial role to ensure Aedes control programmes and encourages the communities to involve active participation. Mobile application mAedesID is developed for identifying the Aedes mosquito species using a deep learning Convolutional Neural Network (CNN) algorithm.
  arXiv  Detail & Related papers  (2023-05-02T14:20:13Z)
• HumBugDB: A Large-scale Acoustic Mosquito Dataset [15.108701811353097]
  This paper presents the first large-scale multi-species dataset of acoustic recordings of mosquitoes tracked continuously in free flight. We present 20 hours of audio recordings that we have expertly labelled and tagged precisely in time. 18 hours of recordings contain annotations from 36 different species.
  arXiv  Detail & Related papers  (2021-10-14T14:18:17Z)
• Project Achoo: A Practical Model and Application for COVID-19 Detection from Recordings of Breath, Voice, and Cough [55.45063681652457]
  We propose a machine learning method to quickly triage COVID-19 using recordings made on consumer devices. The approach combines signal processing methods with fine-tuned deep learning networks and provides methods for signal denoising, cough detection and classification. We have also developed and deployed a mobile application that uses symptoms checker together with voice, breath and cough signals to detect COVID-19 infection.
  arXiv  Detail & Related papers  (2021-07-12T08:07:56Z)
• Spotting adversarial samples for speaker verification by neural vocoders [102.1486475058963]
  We adopt neural vocoders to spot adversarial samples for automatic speaker verification (ASV) We find that the difference between the ASV scores for the original and re-synthesize audio is a good indicator for discrimination between genuine and adversarial samples. Our codes will be made open-source for future works to do comparison.
  arXiv  Detail & Related papers  (2021-07-01T08:58:16Z)
• Facial Masks and Soft-Biometrics: Leveraging Face Recognition CNNs for Age and Gender Prediction on Mobile Ocular Images [53.913598771836924]
  We address the use of selfie ocular images captured with smartphones to estimate age and gender. We adapt two existing lightweight CNNs proposed in the context of the ImageNet Challenge. Some networks are further pre-trained for face recognition, for which very large training databases are available.
  arXiv  Detail & Related papers  (2021-03-31T01:48:29Z)
• Detecting Aedes Aegypti Mosquitoes through Audio Classification with Convolutional Neural Networks [1.0323063834827415]
  A strategy to raise community awareness regarding mosquito proliferation is building a live map of mosquito incidences using smartphone apps and crowdsourcing. In this paper, we explore the possibility of identifying Aedes aegypti mosquitoes using machine learning techniques and audio analysis captured from commercially available smartphones.
  arXiv  Detail & Related papers  (2020-08-19T00:26:21Z)
• Automatic Detection of Aedes aegypti Breeding Grounds Based on Deep Networks with Spatio-Temporal Consistency [2.4858193569899907]
  Aedes aegypti mosquito infects millions of people with diseases such as dengue zika, chikungunya, and urban yellow fever. Main form to combat these diseases is to avoid mosquito reproduction by searching for and eliminating the potential mosquito breeding grounds. In this work, we introduce a comprehensive dataset of aerial videos, acquired with an unmanned aerial vehicle, containing possible mosquito breeding sites.
  arXiv  Detail & Related papers  (2020-07-29T14:30:54Z)
• Rectified Meta-Learning from Noisy Labels for Robust Image-based Plant Disease Diagnosis [64.82680813427054]
  Plant diseases serve as one of main threats to food security and crop production. One popular approach is to transform this problem as a leaf image classification task, which can be addressed by the powerful convolutional neural networks (CNNs) We propose a novel framework that incorporates rectified meta-learning module into common CNN paradigm to train a noise-robust deep network without using extra supervision information.
  arXiv  Detail & Related papers  (2020-03-17T09:51:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.