One of the most promising techniques used in various sciences is deep neural networks
(DNNs). A special type of DNN called a convolutional neural network (CNN) consists of …

Deep learning has become an area of interest to the researchers in the past few years.
Convolutional Neural Network (CNN) is a deep learning approach that is widely used for …

Automatic sleep staging has been often treated as a simple classification problem that aims
at determining the label of individual target polysomnography epochs one at a time. In this …

A considerable challenge in applying deep learning to audio classification is the scarcity of
labeled data. An increasingly popular solution is to learn deep audio embeddings from large …

Correctly identifying sleep stages is important in diagnosing and treating sleep disorders.
This paper proposes a joint classification-and-prediction framework based on convolutional …

We present in this paper an efficient convolutional neural network (CNN) running on time-
frequency image features for automatic sleep stage classification. Opposing to deep …

Human activity recognition is crucial for a better understanding of workers in construction
sites and people in the built environment. Previous studies have been proposed various …

We introduce in this work an efficient approach for audio scene classification using deep
recurrent neural networks. An audio scene is firstly transformed into a sequence of high …

In proportion to the immense construction of spatial structures is the emergence of
catastrophes related to structural damages (eg loose connections), thus rendering personal …

This article proposes an encoder-decoder network model for Acoustic Scene Classification
(ASC), the task of identifying the scene of an audio recording from its acoustic signature. We …