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�…

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�…

Convolutional Neural Networks (CNN) have been applied to diverse machine learning tasks
for different modalities of raw data in an end-to-end fashion. In the audio domain, a raw�…

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�…

The computer vision literature shows that randomly weighted neural networks perform
reasonably as feature extractors. Following this idea, we study how non-trained (randomly�…

The remarkable success of deep convolutional neural networks in image-related
applications has led to their adoption also for sound processing. Typically the input is a time�…

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 can be used for audio signal classification in a variety of ways. It can be used
to detect and classify various types of audio signals such as speech, music, and�…

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�…

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