Deep learning models have improved cutting-edge technologies in many research areas,
but their black-box structure makes it difficult to understand their inner workings and the�…

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�…

In this paper, we study the performance of variants of well-known Convolutional Neural
Network (CNN) architectures on different audio tasks. We show that tuning the Receptive�…

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

Audio classification is regarded as a great challenge in pattern recognition. Although audio
classification tasks are always treated as independent tasks, tasks are essentially related to�…

We investigate supervised learning strategies that improve the training of neural network
audio classifiers on small annotated collections. In particular, we study whether (i) a naive�…

Deep Learning (DL) algorithms have shown impressive performance in diverse domains.
Among them, audio has attracted many researchers over the last couple of decades due to�…

Audio classification is an active research area with a wide range of applications. Over the
past decade, convolutional neural networks (CNNs) have been the de-facto standard�…

While efficient architectures and a plethora of augmentations for end-to-end image
classification tasks have been suggested and heavily investigated, state-of-the-art�…

Audio pattern recognition is an important research topic in the machine learning area, and
includes several tasks such as audio tagging, acoustic scene classification, music�…