754 citations · 3.2k across the 112 of their papers we have counts for
6 papers · 1 filter
A Joint Detection-Classification Model for Audio Tagging of Weakly Labelled Data
Qiuqiang Kong, Yong Xu, Wenwu Wang +1
Audio tagging aims to assign one or several tags to an audio clip. Most of the datasets are weakly labelled, which means only the tags of the clip are known, without knowing the oc…
Discriminative Enhancement for Single Channel Audio Source Separation using Deep Neural Networks
Emad M. Grais, Gerard Roma, Andrew J. R. Simpson +1
The sources separated by most single channel audio source separation techniques are usually distorted and each separated source contains residual signals from the other sources. To…
Hierarchical learning for DNN-based acoustic scene classification
Yong Xu, Qiang Huang, Wenwu Wang +1
In this paper, we present a deep neural network (DNN)-based acoustic scene classification framework. Two hierarchical learning methods are proposed to improve the DNN baseline perf…
Automatic Environmental Sound Recognition: Performance versus Computational Cost
Siddharth Sigtia, Adam M. Stark, Sacha Krstulovic +1
In the context of the Internet of Things (IoT), sound sensing applications are required to run on embedded platforms where notions of product pricing and form factor impose hard co…
Unsupervised Feature Learning Based on Deep Models for Environmental Audio Tagging
Yong Xu, Qiang Huang, Wenwu Wang +4
Environmental audio tagging aims to predict only the presence or absence of certain acoustic events in the interested acoustic scene. In this paper we make contributions to audio t…
Fully DNN-based Multi-label regression for audio tagging
Yong Xu, Qiang Huang, Wenwu Wang +2
Acoustic event detection for content analysis in most cases relies on lots of labeled data. However, manually annotating data is a time-consuming task, which thus makes few annotat…