Classification of environmental sound is an equally challenging task to classification of
human speech or music. In recent years, attention mechanism has gained considerable …

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

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 gaining popularity in the field of information
processing due to its significance in non-speech audio categorization. ESC faces …

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

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

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

Recent years have witnessed a remarkable performance of attention mechanisms for
learning representative and prototypical features for tasks such as the classification of …

Environmental sound classification (ESC) is a challenging problem due to the unstructured
spatial-temporal relations that exist in the sound signals. Recently, many studies have …

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