Complexity of environmental sounds impose numerous challenges for their classification.
The performance of Environmental Sound Classification (ESC) depends greatly on how …

Environmental sound classification (ESC) is a challenging problem due to the complexity of
sounds. The classification performance is heavily dependent on the effectiveness of …

Convolutional neural networks (CNN) are one of the best-performing neural network
architectures for environmental sound classification (ESC). Recently, temporal attention …

Environmental sound classification (ESC) is a challenging problem due to the complex
temporal structure and diverse energy modulation patterns of environmental sounds. In …

Recently, many attention-based deep neural networks have emerged and achieved state-of-
the-art performance in environmental sound classification. The essence of attention …

Automated environmental sound recognition has clear engineering benefits; it allows audio
to be sorted, curated, and searched. Unlike music and language, environmental sound is …

The ability of deep convolutional neural networks (CNNs) to learn discriminative spectro-
temporal patterns makes them well suited to environmental sound classification. However …

Environmental sound classification (ESC) is an important and challenging problem. In
contrast to speech, sound events have noise-like nature and may be produced by a wide …

Environmental sound classification systems often do not perform robustly across different
sound classification tasks and audio signals of varying temporal structures. We introduce a …

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 …