Acoustic scene classification is an intricate problem for a machine. As an emerging field of
research, deep Convolutional Neural Networks (CNN) achieve convincing results. In this …

This technical report describes the ADSPLAB team's submission for Task1 of DCASE2020
challenge. Our acoustic scene classification (ASC) system is based on the convolutional …

Convolutional Neural Networks (CNNs) have been widely applied to audio classification
recently where promising results have been obtained. Previous CNN-based systems mostly …

Acoustic Scene Classification (ASC) aims to classify the environment in which the audio
signals are recorded. Recently, Convolutional Neural Networks (CNNs) have been …

Deep neural networks (DNNs) have been proven to be powerful models for acoustic scene
classification tasks. State-of-the-art DNNs have millions of connections and are …

This technical report describes our submission for TASK1A of DCASE2020 challenge. We
use log-mel spectrograms and a residual network. We follow the idea of McDonnell [1] in …

Convolutional neural networks (CNNs) with log-mel spectrum features have shown
promising results for acoustic scene classification tasks. However, the performance of these …

In this paper, we investigate the use of a multi-task learning framework to address the
DCASE 2022 Task 1 on Low-complexity Acoustic Scene Classification (ASC). Specifically …

In human-computer interaction, acoustic scene classification (ASC) is one of the relevant
research domains. In real life, the recorded audio may include a lot of noise and quiet clips …

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