Shallow neural networks process the features directly, while deep networks extract features
automatically along with the training. Both models suffer from overfitting or poor …

In the last few years, the deep learning (DL) computing paradigm has been deemed the
Gold Standard in the machine learning (ML) community. Moreover, it has gradually become …

Humans perceive the world by concurrently processing and fusing high-dimensional inputs
from multiple modalities such as vision and audio. Machine perception models, in stark …

Variation in avian diversity in space and time is commonly used as a metric to assess
environmental changes. Conventionally, such data were collected by expert observers, but …

The rapidly evolving field of sound classification has greatly benefited from the methods of
other domains. Today, the trend is to fuse domain-specific tasks and approaches together …

Most existing datasets for sound event recognition (SER) are relatively small and/or domain-
specific, with the exception of AudioSet, based on over 2 M tracks from YouTube videos and …

This article is a survey of deep learning methods for single and multiple sound source
localization, with a focus on sound source localization in indoor environments, where …

As a subfield of machine learning, reinforcement learning (RL) aims at optimizing decision
making by using interaction samples of an agent with its environment and the potentially …

Objective Employing transfer learning (TL) with convolutional neural networks (CNNs), well-
trained on non-medical ImageNet dataset, has shown promising results for medical image …

Discovering new treatments and personalizing existing ones is one of the major goals of
modern clinical research. In the last decade, Artificial Intelligence (AI) has enabled the …