Previous DCASE challenges contributed to an increase in the performance of acoustic
scene classification systems. State-of-the-art classifiers demand significant processing …

Acoustic Scene Classification (ASC) aim to recognize an acoustic scene in audio signal
records. The acoustic scene is a mixture of background sounds and various sound events …

This report describes our contribution to the development of audio scene classification
methods for the DCASE 2018 Challenge Task 1A. The proposed systems for this task are …

Deep neural networks (DNNs) are successful in applications with matching inference and
training distributions. In realworld scenarios, DNNs have to cope with truly new data …

It is a practical research topic how to deal with multi-device audio inputs by a single acoustic
scene classification system with efficient design. In this work, we propose Residual …

In this paper, we presents a low-complexity deep learning frameworks for acoustic scene
classification (ASC). The proposed framework can be separated into three main steps: Front …

Deep learning (DL) is key for the recent boost of acoustic scene classification (ASC)
performance. Especially, convolutional neural networks (CNNs) are widely adopted with …

This work proposes bag-of-features deep learning models for acoustic scene classification
(ASC)–identifying recording locations by analysing background sound. We explore the effect …

Ensemble semantic features are useful for acoustic scene classification. In this paper, we
proposed a multi-scale fusion and channel weighted CNN framework. The framework …

Acoustic scene classification (ASC) is a problem related to the field of machine listening
whose objective is to classify/tag an audio clip in a predefined label describing a scene …