Automatic environmental sound classification (ESC) is one of the upcoming areas of
research as most of the traditional studies are focused on speech and music signals …

In this paper, we present an end-to-end approach for environmental sound classification
based on a 1D Convolution Neural Network (CNN) that learns a representation directly from …

In this paper, we show that ImageNet-Pretrained standard deep CNN models can be used
as strong baseline networks for audio classification. Even though there is a significant …

Deep neural networks in deep learning have been widely demonstrated to have higher
accuracy and distinct advantages over traditional machine learning methods in extracting …

In this study, an effective approach of spectral images based on environmental sound
classification using Convolutional Neural Networks (CNN) with meaningful data …

Environmental sound classification is one of the important issues in the audio recognition
field. Compared with structured sounds such as speech and music, the time–frequency …

Background Automatic diagnosis of depression based on speech can complement mental
health treatment methods in the future. Previous studies have reported that acoustic …

Environmental sounds are being used widely in our lives. It is especially used in tasks such
as managing smart cities, location determination, surveillance systems, machine hearing …

Environmental Sound Classification (ESC) is an active research area in the audio domain
and has seen a lot of progress in the past years. However, many of the existing approaches …

Automatic bird sound classification plays an important role in monitoring and further
protecting biodiversity. Recent advances in acoustic sensor networks and deep learning …