The work assessed seven classical classifiers and two beamforming algorithms for detecting surveillance sound events.
Self-supervised learning has advanced rapidly, with several results beating supervised models for pre-training feature representations.
As deep neural networks become widely adopted for solving most problems in computer vision and audio-understanding, there are rising concerns about their potential vulnerability.
This layer aims at being the input layer of convolutional neural networks for audio applications.
Formal verification techniques that compute provable guarantees on properties of machine learning models, like robustness to norm-bounded adversarial perturbations, have yielded impressive results.
We introduce a unified probabilistic approach for deep continual learning based on variational Bayesian inference with open set recognition.
Recent progress in visual grounding techniques and Audio Understanding are enabling machines to understand shared semantic concepts and listen to the various sensory events in the environment.
With the recent advancements in Artificial Intelligence (AI), Intelligent Virtual Assistants (IVA) such as Alexa, Google Home, etc., have become a ubiquitous part of many homes.
To the best of our knowledge this is the largest study on data augmentation for CNNs in animal audio classification audio datasets using the same set of classifiers and parameters.