Acoustic scene classification (ASC) has gained significant interest recently due to its diverse
applications. Various audio signal processing and machine learning methods have been …

This paper presents an analysis of the Low-Complexity Acoustic Scene Classification task in
DCASE 2022 Challenge. The task was a continuation from the previous years, but the low …

Neural networks do not generalize well to unseen data with domain shifts—a longstanding
problem in machine learning and AI. To overcome the problem, we propose MixStyle, a …

Knowledge Distillation (KD) is known for its ability to compress large models into low-
complexity solutions while preserving high predictive performance. In Acoustic Scene …

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 …

In this paper, we propose lightweight deep neural networks for Acoustic Scene Classification
(ASC) and a visualization method for presenting a sound scene context. To this end, we first …

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 …

Acoustic scene classification (ASC) predominantly relies on supervised approaches.
However, acquiring labeled data for training ASC models is often costly and time …

The task 1 of DCASE 2023 Challenge incorporates a weighted average ranking of accuracy
and complexity, which encourages participants to build efficient systems for acoustic scene …

An acoustic scene is inferred by detecting properties combining diverse sounds and
acoustic environments. This study is intended to discover these properties effectively using …