33 research outputs found
Mosquito detection with low-cost smartphones: data acquisition for malaria research
Mosquitoes are a major vector for malaria, causing hundreds of thousands of
deaths in the developing world each year. Not only is the prevention of
mosquito bites of paramount importance to the reduction of malaria transmission
cases, but understanding in more forensic detail the interplay between malaria,
mosquito vectors, vegetation, standing water and human populations is crucial
to the deployment of more effective interventions. Typically the presence and
detection of malaria-vectoring mosquitoes is only quantified by hand-operated
insect traps or signified by the diagnosis of malaria. If we are to gather
timely, large-scale data to improve this situation, we need to automate the
process of mosquito detection and classification as much as possible. In this
paper, we present a candidate mobile sensing system that acts as both a
portable early warning device and an automatic acoustic data acquisition
pipeline to help fuel scientific inquiry and policy. The machine learning
algorithm that powers the mobile system achieves excellent off-line
multi-species detection performance while remaining computationally efficient.
Further, we have conducted preliminary live mosquito detection tests using
low-cost mobile phones and achieved promising results. The deployment of this
system for field usage in Southeast Asia and Africa is planned in the near
future. In order to accelerate processing of field recordings and labelling of
collected data, we employ a citizen science platform in conjunction with
automated methods, the former implemented using the Zooniverse platform,
allowing crowdsourcing on a grand scale.Comment: Presented at NIPS 2017 Workshop on Machine Learning for the
Developing Worl
Mosquito Detection with Neural Networks: The Buzz of Deep Learning
Many real-world time-series analysis problems are characterised by scarce
data. Solutions typically rely on hand-crafted features extracted from the time
or frequency domain allied with classification or regression engines which
condition on this (often low-dimensional) feature vector. The huge advances
enjoyed by many application domains in recent years have been fuelled by the
use of deep learning architectures trained on large data sets. This paper
presents an application of deep learning for acoustic event detection in a
challenging, data-scarce, real-world problem. Our candidate challenge is to
accurately detect the presence of a mosquito from its acoustic signature. We
develop convolutional neural networks (CNNs) operating on wavelet
transformations of audio recordings. Furthermore, we interrogate the network's
predictive power by visualising statistics of network-excitatory samples. These
visualisations offer a deep insight into the relative informativeness of
components in the detection problem. We include comparisons with conventional
classifiers, conditioned on both hand-tuned and generic features, to stress the
strength of automatic deep feature learning. Detection is achieved with
performance metrics significantly surpassing those of existing algorithmic
methods, as well as marginally exceeding those attained by individual human
experts.Comment: For data and software related to this paper, see
http://humbug.ac.uk/kiskin2017/. Submitted as a conference paper to ECML 201
Dual Bayesian ResNet: a deep learning approach to heart murmur detection
This study presents our team PathToMyHeart’s contribution to the George B. Moody PhysioNet Challenge 2022. Two models are implemented. The first model is a Dual Bayesian ResNet (DBRes), where each patient’s recording is segmented into overlapping log mel spectrograms. These undergo two binary classifications: present versus unknown or absent, and unknown versus present or absent. The classifications are aggregated to give a patient’s final classification. The second model is the output of DBRes integrated with demographic data and signal features using XGBoost. DBRes achieved our best weighted accuracy of 0.771 on the hidden test set for murmur classification, which placed us fourth for the murmur task. (On the clinical outcome task, which we neglected, we scored 17th with costs of 12637.) On our held-out subset of the training set, integrating the demographic data and signal features improved DBRes’s accuracy from 0.762 to 0.820. However, this decreased DBRes’s weighted accuracy from 0.780 to 0.749. Our results demonstrate that log mel spectrograms are an effective representation of heart sound recordings, Bayesian networks provide strong supervised classification performance, and treating the ternary classification as two binary classifications increases performance on the weighted accuracy
The ACM Multimedia 2022 Computational Paralinguistics Challenge: vocalisations, stuttering, activity, & mosquitoes
The ACM Multimedia 2022 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the Vocalisations and Stuttering Sub-Challenges, a classification on human non-verbal vocalisations and speech has to be made; the Activity Sub-Challenge aims at beyond-audio human activity recognition from smartwatch sensor data; and in the Mosquitoes Sub-Challenge, mosquitoes need to be detected. We describe the Sub-Challenges, baseline feature extraction, and classifiers based on the 'usual' ComParE and BoAW features, the auDeep toolkit, and deep feature extraction from pre-trained CNNs using the DeepSpectrum toolkit; in addition, we add end-to-end sequential modelling, and a log-mel-128-BNN
A large-scale and PCR-referenced vocal audio dataset for COVID-19
The UK COVID-19 Vocal Audio Dataset is designed for the training and
evaluation of machine learning models that classify SARS-CoV-2 infection status
or associated respiratory symptoms using vocal audio. The UK Health Security
Agency recruited voluntary participants through the national Test and Trace
programme and the REACT-1 survey in England from March 2021 to March 2022,
during dominant transmission of the Alpha and Delta SARS-CoV-2 variants and
some Omicron variant sublineages. Audio recordings of volitional coughs,
exhalations, and speech were collected in the 'Speak up to help beat
coronavirus' digital survey alongside demographic, self-reported symptom and
respiratory condition data, and linked to SARS-CoV-2 test results. The UK
COVID-19 Vocal Audio Dataset represents the largest collection of SARS-CoV-2
PCR-referenced audio recordings to date. PCR results were linked to 70,794 of
72,999 participants and 24,155 of 25,776 positive cases. Respiratory symptoms
were reported by 45.62% of participants. This dataset has additional potential
uses for bioacoustics research, with 11.30% participants reporting asthma, and
27.20% with linked influenza PCR test results.Comment: 37 pages, 4 figure