no code implementations • 7 Dec 2019 • Miao Du, Qin Yu, Shaomin Fei, Chen Wang, Xiao-Feng Gong, Ruisen Luo
Nowadays, we mainly use various convolution neural network (CNN) structures to extract features from radio data or spectrogram in AMR.
no code implementations • 27 Sep 2019 • Ruisen Luo, Tao Hu, Zuodong Tang, Chen Wang, Xiaofeng Gong, Haiyan Tu
To solve the problem of inaccurate recognition of types of communication signal modulation, a RNN neural network recognition algorithm combining residual block network with attention mechanism is proposed.
no code implementations • 9 Aug 2019 • Chen Wang, Chengyuan Deng, Zhoulu Yu, Dafeng Hui, Xiaofeng Gong, Ruisen Luo
In addition, the proposed method has other preferred properties such as special advantages in dealing with highly imbalanced data, and it pioneers the research on the regularization for dynamic ensemble methods.
no code implementations • 10 Jul 2019 • Ruisen Luo, Tianran Sun, Chen Wang, Miao Du, Zuodong Tang, Kai Zhou, Xiao-Feng Gong, Xiaomei Yang
The key idea is that, in addition to the conventional attention mechanism, information of layers prior to feature extraction and LSTM are introduced into attention weights calculations.
Ranked #4 on Keyword Spotting on Google Speech Commands (Google Speech Commands V2 20 metric)
no code implementations • 1 Mar 2018 • Chen Wang, Xiaomei Yang, Shaomin Fei, Kai Zhou, Xiao-Feng Gong, Miao Du, Ruisen Luo
Furthermore, to compute quantization results with a given amount of values/clusters, this paper designed an iterative method and a clustering-based method, and both of them are built on sparse least square.