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Found 9 papers, 4 papers with code
Contrasting Deep Learning Models for Direct Respiratory Insufficiency Detection Versus Blood Oxygen Saturation Estimation
We contrast high effectiveness of state of the art deep learning architectures designed for general audio classification tasks, refined for respiratory insufficiency (RI) detection and blood oxygen saturation (SpO2) estimation and classification through automated audio analysis.
Discriminant audio properties in deep learning based respiratory insufficiency detection in Brazilian Portuguese
This work investigates Artificial Intelligence (AI) systems that detect respiratory insufficiency (RI) by analyzing speech audios, thus treating speech as a RI biomarker.
Acoustic models of Brazilian Portuguese Speech based on Neural Transformers
In this work, we build an acoustic model of Brazilian Portuguese Speech through a Transformer neural network.
Pretrained audio neural networks for Speech emotion recognition in Portuguese
This dataset contains about $50$ minutes of Brazilian Portuguese speech.
Audio MFCC-gram Transformers for respiratory insufficiency detection in COVID-19
This work explores speech as a biomarker and investigates the detection of respiratory insufficiency (RI) by analyzing speech samples.
The Influence of Memory in Multi-Agent Consensus
Multi-agent consensus problems can often be seen as a sequence of autonomous and independent local choices between a finite set of decision options, with each local choice undertaken simultaneously, and with a shared goal of achieving a global consensus state.
Improving Gradient Estimation in Evolutionary Strategies With Past Descent Directions
Finally, we evaluate our approach empirically on MNIST and reinforcement learning tasks and show that it considerably improves the gradient estimation of ES at no extra computational cost.
Optimal Kronecker-Sum Approximation of Real Time Recurrent Learning
In contrast, the online training algorithm Real Time Recurrent Learning (RTRL) provides untruncated gradients, with the disadvantage of impractically large computational costs.
The linear hidden subset problem for the (1+1) EA with scheduled and adaptive mutation rates
For the first setup, we give a schedule that achieves a runtime of $(1\pm o(1))\beta n \ln n$, where $\beta \approx 3. 552$, which is an asymptotic improvement over the runtime of the static setup.
