Acoustic scene classification (ASC) using front-end timefrequency features and back-end
neural network classifiers has demonstrated good performance in recent years. However a …

Neural networks have been proven to be powerful models for acoustic scene classification
tasks, but are still limited by the lack of ability to be temporally invariant to the audio data. In …

In our submission to the DCASE 2019 Task 1a, we have explored the use of four different
deep learning based neural networks architectures: Vgg12, ResNet50, AclNet, and …

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

Abstract Convolutional Neural Networks (CNNs) have been widely investigated on Acoustic
Scene Classification (ASC). Where the convolutional operation can extract useful semantic …

Various attention mechanisms are being widely applied to acoustic scene classification.
However, we empirically found that the attention mechanism can excessively discard …

This technical report describes our approach to Tasks 1a in the 2020 DCASE acoustic scene
classification challenge. We have incorporated few more training techniques based on our …

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) is a topic related to the field of machine listening whose
important role is to recognize and categorize audio data in a predefined label which …

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 …