4 research outputs found
Environment Knowledge-Driven Generic Models to Detect Coughs From Audio Recordings
Goal: Millions of people are dying due to res- piratory diseases, such as COVID-19 and asthma, which are often characterized by some common symptoms, including coughing. Therefore, objective reporting of cough symp- toms utilizing environment-adaptive machine-learning models with microphone sensing can directly contribute to respiratory disease diagnosis and patient care. Methods: In this work, we present three generic modeling approaches – unguided, semi-guided, and guided approaches consid- ering three potential scenarios, i.e., when a user has no prior knowledge, some knowledge, and detailed knowledge about the environments, respectively. Results: From detailed analysis with three datasets, we find that guided models are up to 28% more accurate than the unguided models. We find reasonable performance when assessing the applicability of our models using three additional datasets, including two open-sourced cough datasets. Con- clusions: Though guided models outperform other models, they require a better understanding of the environment
LCANets++: Robust Audio Classification using Multi-layer Neural Networks with Lateral Competition
Audio classification aims at recognizing audio signals, including speech
commands or sound events. However, current audio classifiers are susceptible to
perturbations and adversarial attacks. In addition, real-world audio
classification tasks often suffer from limited labeled data. To help bridge
these gaps, previous work developed neuro-inspired convolutional neural
networks (CNNs) with sparse coding via the Locally Competitive Algorithm (LCA)
in the first layer (i.e., LCANets) for computer vision. LCANets learn in a
combination of supervised and unsupervised learning, reducing dependency on
labeled samples. Motivated by the fact that auditory cortex is also sparse, we
extend LCANets to audio recognition tasks and introduce LCANets++, which are
CNNs that perform sparse coding in multiple layers via LCA. We demonstrate that
LCANets++ are more robust than standard CNNs and LCANets against perturbations,
e.g., background noise, as well as black-box and white-box attacks, e.g.,
evasion and fast gradient sign (FGSM) attacks.Comment: This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessibl
Environment Knowledge-Driven Generic Models to Detect Coughs From Audio Recordings
Goal: Millions of people are dying due to respiratory diseases, such as COVID-19 and asthma, which are often characterized by some common symptoms, including coughing. Therefore, objective reporting of cough symptoms utilizing environment-adaptive machine-learning models with microphone sensing can directly contribute to respiratory disease diagnosis and patient care. Methods: In this work, we present three generic modeling approaches – unguided, semi-guided, and guided approaches considering three potential scenarios, i.e., when a user has no prior knowledge, some knowledge, and detailed knowledge about the environments, respectively. Results: From detailed analysis with three datasets, we find that guided models are up to 28% more accurate than the unguided models. We find reasonable performance when assessing the applicability of our models using three additional datasets, including two open-sourced cough datasets. Conclusions: Though guided models outperform other models, they require a better understanding of the environment
Environment Knowledge-Driven Generic Models to Detect Coughs from Audio Recordings
Goal: Millions of people are dying due to respiratory diseases, such as COVID-19 and asthma, which are often characterized by some common symptoms, including coughing. Therefore, objective reporting of cough symptoms utilizing environment-adaptive machine-learning models with microphone sensing can directly contribute to respiratory disease diagnosis and patient care. Methods: In this work, we present three generic modeling approaches - unguided, semi-guided, and guided approaches considering three potential scenarios, i.e., when a user has no prior knowledge, some knowledge, and detailed knowledge about the environments, respectively. Results: From detailed analysis with three datasets, we find that guided models are up to 28% more accurate than the unguided models. We find reasonable performance when assessing the applicability of our models using three additional datasets, including two open-sourced cough datasets. Conclusions: Though guided models outperform other models, they require a better understanding of the environment