Music, speech, and acoustic scene sound are often handled separately in the audio domain
because of their different signal characteristics. However, as the image domain grows�…

End-to-end learning with convolutional neural networks (CNNs) has become a standard
approach in image classification. However, in audio classification, CNN-based models that�…

Over the past two decades, CNN architectures have produced compelling models of sound
perception and cognition, learning hierarchical organizations of features. Analogous to�…

In this paper, we show that ImageNet-Pretrained standard deep CNN models can be used
as strong baseline networks for audio classification. Even though there is a significant�…

Deep learning has dramatically improved the performance of sounds recognition. However,
learning acoustic models directly from the raw waveform is still challenging. Current�…

We propose an efficient end-to-end convolutional neural network architecture, AclNet, for
audio classification. When trained with our data augmentation and regularization, we�…

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

Deep learning has celebrated resounding successes in many application areas of relevance
to the Internet of Things (IoT), such as computer vision and machine listening. These�…

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�…