In the last few years, deep learning has led to very good performance on a variety of
problems, such as visual recognition, speech recognition and natural language processing …

Given the recent surge in developments of deep learning, this paper provides a review of the
state-of-the-art deep learning techniques for audio signal processing. Speech, music, and …

Convolutional Neural Networks (CNNs) have been recently introduced in the domain of
session-based next item recommendation. An ordered collection of past items the user has …

Abstract El Niño-Southern Oscillation (ENSO), which is one of the main drivers of Earth's
inter-annual climate variability, often causes a wide range of climate anomalies, and the …

One of the most difficult speech recognition tasks is accurate recognition of human to human
communication. Advances in deep learning over the last few years have produced major …

Background and objective SARS-CoV-2, a novel strain of coronavirus' also called
coronavirus disease 19 (COVID-19), a highly contagious pathogenic respiratory viral …

In this work, we perform an empirical comparison among the CTC, RNN-Transducer, and
attention-based Seq2Seq models for end-to-end speech recognition. We show that, without …

For supervised speech enhancement, contextual information is important for accurate mask
estimation or spectral mapping. However, commonly used deep neural networks (DNNs) are …

In this paper, we summarize recent progresses made in deep learning based acoustic
models and the motivation and insights behind the surveyed techniques. We first discuss …

Interest in automatic crowd behaviour analysis has grown considerably in the last few years.
Crowd behaviour analysis has become an integral part all over the world for ensuring …