35 citations · 79 across the 7 of their papers we have counts for
8 papers
Unsupervised Voice-Face Representation Learning by Cross-Modal Prototype Contrast
Boqing Zhu, Kele Xu, Changjian Wang +4
We present an approach to learn voice-face representations from the talking face videos, without any identity labels. Previous works employ cross-modal instance discrimination task…
Multi-task Pre-training Language Model for Semantic Network Completion
Da Li, Sen Yang, Kele Xu +3
Semantic networks, such as the knowledge graph, can represent the knowledge leveraging the graph structure. Although the knowledge graph shows promising values in natural language…
Audio Tagging by Cross Filtering Noisy Labels
Boqing Zhu, Kele Xu, Qiuqiang Kong +2
High quality labeled datasets have allowed deep learning to achieve impressive results on many sound analysis tasks. Yet, it is labor-intensive to accurately annotate large amount…
Weakly supervised CRNN system for sound event detection with large-scale unlabeled in-domain data
Dezhi Wang, Lilun Zhang, Changchun Bao +3
Sound event detection (SED) is typically posed as a supervised learning problem requiring training data with strong temporal labels of sound events. However, the production of data…
General audio tagging with ensembling convolutional neural network and statistical features
Kele Xu, Boqing Zhu, Qiuqiang Kong +4
Audio tagging aims to infer descriptive labels from audio clips. Audio tagging is challenging due to the limited size of data and noisy labels. In this paper, we describe our solut…
Environmental Sound Classification Based on Multi-temporal Resolution Convolutional Neural Network Combining with Multi-level Features
Boqing Zhu, Kele Xu, Dezhi Wang +3
Motivated by the fact that characteristics of different sound classes are highly diverse in different temporal scales and hierarchical levels, a novel deep convolutional neural net…
Mixup-Based Acoustic Scene Classification Using Multi-Channel Convolutional Neural Network
Kele Xu, Dawei Feng, Haibo Mi +5
Audio scene classification, the problem of predicting class labels of audio scenes, has drawn lots of attention during the last several years. However, it remains challenging and f…
Learning Environmental Sounds with Multi-scale Convolutional Neural Network
Boqing Zhu, Changjian Wang, Feng Liu +3
Deep learning has dramatically improved the performance of sounds recognition. However, learning acoustic models directly from the raw waveform is still challenging. Current wavefo…