Neural networks are prone to overfitting and memorizing data patterns. To avoid over-fitting
and enhance their generalization and performance, various methods have been suggested …

Inspired by the recent progress in self-supervised learning for computer vision that
generates supervision using data augmentations, we explore a new general-purpose audio …

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

With the popularity of using deep learning-based models in various categorization problems
and their proven robustness compared to conventional methods, a growing number of …

The adoption of the environmental sound classification (ESC) tasks increases very rapidly
over recent years due to its broad range of applications in our daily routine life. ESC is also …

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 …

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

In this paper, we propose MixSpeech, a simple yet effective data augmentation method
based on mixup for automatic speech recognition (ASR). MixSpeech trains an ASR model …

Automated bioacoustics classification has received increasing attention from the research
community in recent years due its cross-disciplinary nature and its diverse application …

In this paper, we propose a model for the Environment Sound Classification Task (ESC) that
consists of multiple feature channels given as input to a Deep Convolutional Neural Network …