Abstract Visual Pattern Recognition Networks (VPRNs) are widely used in various visual
data based applications such as computer vision and edge AI. VPRNs help to enhance a …

Deep learning has achieved notable success across academia, medicine, and industry. Its
ability to identify complex patterns in large-scale data and to manage millions of parameters …

Speech enhancement is regarded as the key to the quality of digital communication and is
gaining increasing attention in the research field of audio processing. In this paper, we …

Recording the trajectory of table tennis balls in real-time enables the analysis of the
opponent's attacking characteristics and weaknesses. The current analysis of the ball paths …

In computer vision, convolutional neural networks (CNN) such as ConvNeXt, have been
able to surpass state-of-the-art transformers, partly thanks to depthwise separable …

Designing lightweight models that require limited computational resources and can operate
on edge devices is a major trajectory in deep learning research. In the context of Acoustic …

The recognition of surgical triplets plays a critical role in the practical application of surgical
videos. It involves the sub-tasks of recognizing instruments, verbs, and targets, while …

As sound recognition and classification models become more complex, their performance
improves, but this comes with higher computational demands. This work addresses the …

Augmentation and knowledge distillation (KD) are well-established techniques employed in
audio classification tasks, aimed at enhancing performance and reducing model sizes on …

In this technical report, we describe the CP-JKU team's submission for Task 1 Low-
Complexity Acoustic Scene Classification of the DCASE 23 challenge. We introduce a novel …