754 citations · 3.2k across the 112 of their papers we have counts for
8 papers · 2 filters
A joint separation-classification model for sound event detection of weakly labelled data
Qiuqiang Kong, Yong Xu, Wenwu Wang +1
Source separation (SS) aims to separate individual sources from an audio recording. Sound event detection (SED) aims to detect sound events from an audio recording. We propose a jo…
Audio Set classification with attention model: A probabilistic perspective
Qiuqiang Kong, Yong Xu, Wenwu Wang +1
This paper investigates the classification of the Audio Set dataset. Audio Set is a large scale weakly labelled dataset of sound clips. Previous work used multiple instance learnin…
Multi-Resolution Fully Convolutional Neural Networks for Monaural Audio Source Separation
Emad M. Grais, Hagen Wierstorf, Dominic Ward +1
In deep neural networks with convolutional layers, each layer typically has fixed-size/single-resolution receptive field (RF). Convolutional layers with a large RF capture global i…
Large-scale weakly supervised audio classification using gated convolutional neural network
Yong Xu, Qiuqiang Kong, Wenwu Wang +1
In this paper, we present a gated convolutional neural network and a temporal attention-based localization method for audio classification, which won the 1st place in the large-sca…
Surrey-cvssp system for DCASE2017 challenge task4
Yong Xu, Qiuqiang Kong, Wenwu Wang +1
In this technique report, we present a bunch of methods for the task 4 of Detection and Classification of Acoustic Scenes and Events 2017 (DCASE2017) challenge. This task evaluates…
Attention and Localization based on a Deep Convolutional Recurrent Model for Weakly Supervised Audio Tagging
Yong Xu, Qiuqiang Kong, Qiang Huang +2
Audio tagging aims to perform multi-label classification on audio chunks and it is a newly proposed task in the Detection and Classification of Acoustic Scenes and Events 2016 (DCA…
Single Channel Audio Source Separation using Convolutional Denoising Autoencoders
Emad M. Grais, Mark D. Plumbley
Deep learning techniques have been used recently to tackle the audio source separation problem. In this work, we propose to use deep fully convolutional denoising autoencoders (CDA…
Convolutional Gated Recurrent Neural Network Incorporating Spatial Features for Audio Tagging
Yong Xu, Qiuqiang Kong, Qiang Huang +2
Environmental audio tagging is a newly proposed task to predict the presence or absence of a specific audio event in a chunk. Deep neural network (DNN) based methods have been succ…