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�…

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�…

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

The paper investigates retraining options and the performance of pre-trained Convolutional
Neural Networks (CNNs) for sound classification. CNNs were initially designed for image�…

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�…

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�…

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�…

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�…

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