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

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�…

In the past decade, convolutional neural networks (CNNs) have been widely adopted as the
main building block for end-to-end audio classification models, which aim to learn a direct�…

Mel-filterbanks are fixed, engineered audio features which emulate human perception and
have been used through the history of audio understanding up to today. However, their�…

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�…

Recent advancements in deep learning have shown great potential for audio applications,
improving the accuracy of previous solutions for tasks such as music transcription, beat�…

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�…

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�…