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Found 12 papers, 6 papers with code
Few-shot bioacoustic event detection at the DCASE 2023 challenge
Few-shot bioacoustic event detection consists in detecting sound events of specified types, in varying soundscapes, while having access to only a few examples of the class of interest.
Dual Bayesian ResNet: A Deep Learning Approach to Heart Murmur Detection
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.
Ranked #1 on
Classify murmurs
on CirCor DigiScope
Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers
Recent work has reported that AI classifiers trained on audio recordings can accurately predict severe acute respiratory syndrome coronavirus 2 (SARSCoV2) infection status.
Statistical Design and Analysis for Robust Machine Learning: A Case Study from COVID-19
Since early in the coronavirus disease 2019 (COVID-19) pandemic, there has been interest in using artificial intelligence methods to predict COVID-19 infection status based on vocal audio signals, for example cough recordings.
A large-scale and PCR-referenced vocal audio dataset for COVID-19
The UK COVID-19 Vocal Audio Dataset represents the largest collection of SARS-CoV-2 PCR-referenced audio recordings to date.
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.
HumBugDB: A Large-scale Acoustic Mosquito Dataset
Our extensive dataset is both challenging to machine learning researchers focusing on acoustic identification, and critical to entomologists, geo-spatial modellers and other domain experts to understand mosquito behaviour, model their distribution, and manage the threat they pose to humans.
HumBug Zooniverse: a crowd-sourced acoustic mosquito dataset
Mosquitoes are the only known vector of malaria, which leads to hundreds of thousands of deaths each year.
Super-resolution of Time-series Labels for Bootstrapped Event Detection
Solving real-world problems, particularly with deep learning, relies on the availability of abundant, quality data.
Cost-sensitive detection with variational autoencoders for environmental acoustic sensing
Environmental acoustic sensing involves the retrieval and processing of audio signals to better understand our surroundings.
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.
Mosquito Detection with Neural Networks: The Buzz of Deep Learning
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.
