HumBug Zooniverse: a crowd-sourced acoustic mosquito dataset

• URL: http://arxiv.org/abs/2001.04733v2
• Date: Fri, 14 Feb 2020 14:11:11 GMT
• Title: HumBug Zooniverse: a crowd-sourced acoustic mosquito dataset
• Authors: Ivan Kiskin, Adam D. Cobb, Lawrence Wang, Stephen Roberts
• Abstract summary: We release a new dataset of mosquito audio recordings. With over a thousand contributors, we obtained 195,434 labels of two second duration, of which approximately 10 percent signify mosquito events. We present an example use of the dataset, in which we train a convolutional neural network on log-Mel features, showcasing the information content of the labels.
• Score: 5.3909333359654275
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mosquitoes are the only known vector of malaria, which leads to hundreds of thousands of deaths each year. Understanding the number and location of potential mosquito vectors is of paramount importance to aid the reduction of malaria transmission cases. In recent years, deep learning has become widely used for bioacoustic classification tasks. In order to enable further research applications in this field, we release a new dataset of mosquito audio recordings. With over a thousand contributors, we obtained 195,434 labels of two second duration, of which approximately 10 percent signify mosquito events. We present an example use of the dataset, in which we train a convolutional neural network on log-Mel features, showcasing the information content of the labels. We hope this will become a vital resource for those researching all aspects of malaria, and add to the existing audio datasets for bioacoustic detection and signal processing.

Related papers

• Spatial Implicit Neural Representations for Global-Scale Species Mapping [72.92028508757281]
  Given a set of locations where a species has been observed, the goal is to build a model to predict whether the species is present or absent at any location. Traditional methods struggle to take advantage of emerging large-scale crowdsourced datasets. We use Spatial Implicit Neural Representations (SINRs) to jointly estimate the geographical range of 47k species simultaneously.
  arXiv  Detail & Related papers  (2023-06-05T03:36:01Z)
• 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)
• Measures of Information Reflect Memorization Patterns [53.71420125627608]
  We show that the diversity in the activation patterns of different neurons is reflective of model generalization and memorization. Importantly, we discover that information organization points to the two forms of memorization, even for neural activations computed on unlabelled in-distribution examples.
  arXiv  Detail & Related papers  (2022-10-17T20:15:24Z)
• A deep convolutional neural network for classification of Aedes albopictus mosquitoes [1.6758573326215689]
  We introduce the application of two Deep Convolutional Neural Networks in a comparative study to automate the classification task. We use the transfer learning principle to train two state-of-the-art architectures on the data provided by the Mosquito Alert project. In addition, we applied explainable models based on the Grad-CAM algorithm to visualise the most discriminant regions of the classified images.
  arXiv  Detail & Related papers  (2021-10-29T17:58:32Z)
• On the use of uncertainty in classifying Aedes Albopictus mosquitoes [1.6758573326215689]
  Convolutional neural networks (CNNs) have been used by several studies to recognise mosquitoes in images. This paper proposes using the Monte Carlo Dropout method to estimate the uncertainty scores in order to rank the classified samples.
  arXiv  Detail & Related papers  (2021-10-29T16:58:25Z)
• 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)
• Relational Subsets Knowledge Distillation for Long-tailed Retinal Diseases Recognition [65.77962788209103]
  We propose class subset learning by dividing the long-tailed data into multiple class subsets according to prior knowledge. It enforces the model to focus on learning the subset-specific knowledge. The proposed framework proved to be effective for the long-tailed retinal diseases recognition task.
  arXiv  Detail & Related papers  (2021-04-22T13:39:33Z)
• Discriminative Singular Spectrum Classifier with Applications on Bioacoustic Signal Recognition [67.4171845020675]
  We present a bioacoustic signal classifier equipped with a discriminative mechanism to extract useful features for analysis and classification efficiently. Unlike current bioacoustic recognition methods, which are task-oriented, the proposed model relies on transforming the input signals into vector subspaces. The validity of the proposed method is verified using three challenging bioacoustic datasets containing anuran, bee, and mosquito species.
  arXiv  Detail & Related papers  (2021-03-18T11:01:21Z)
• 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)
• Cross-lingual Transfer Learning for COVID-19 Outbreak Alignment [90.12602012910465]
  We train on Italy's early COVID-19 outbreak through Twitter and transfer to several other countries. Our experiments show strong results with up to 0.85 Spearman correlation in cross-country predictions.
  arXiv  Detail & Related papers  (2020-06-05T02:04:25Z)

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.