Environmental sound classification (ESC) is an increasingly relevant field of research in
recent years but has high computational overhead in its classification of environmental …

Environmental sound classification (ESC) is an important research problem with a broad
range of applications including audio-based surveillance, audio-visual systems, smart …

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

Environmental Sound Classification (ESC) is a challenging field of research in non-speech
audio processing. Most of current research in ESC focuses on designing deep models with …

For environmental sound classification, CNNs have become the most successful
architecture. By regarding the CNN features as a collection of nodes arranged on a 2D time …

This paper introduces a novel knowledge distillation (KD) framework that distills the
knowledge from multiple deep models trained on spectrogram features obtained by dividing …

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 …

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

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 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 …