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

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�…

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�…

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�…

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

Speech music discrimination is a traditional task in audio analytics, useful for a wide range
of applications, such as automatic speech recognition and radio broadcast monitoring, that�…

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�…

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

Research in sound classification and recognition is rapidly advancing in the field of pattern
recognition. One important area in this field is environmental sound recognition, whether it�…