Temporal Convolutional Network for Speech Bandwidth Extension  

作　　者：Chundong Xu Cheng Zhu Xianpeng Ling Dongwen Ying 

机构地区：[1]School of Information Engineering,Jiangxi University of Science and Technology,Jiangxi 341000,China [2]School of Electronic,Electrical and Communication Engineering,University of Chinese Academy of Sciences,Beijing 100049,China

出　　处：《China Communications》2023年第11期142-150,共9页中国通信（英文版）

摘　　要：In the field of speech bandwidth exten-sion,it is difficult to achieve high speech quality based on the shallow statistical model method.Although the application of deep learning has greatly improved the extended speech quality,the high model complex-ity makes it infeasible to run on the client.In order to tackle these issues,this paper proposes an end-to-end speech bandwidth extension method based on a temporal convolutional neural network,which greatly reduces the complexity of the model.In addition,a new time-frequency loss function is designed to en-able narrowband speech to acquire a more accurate wideband mapping in the time domain and the fre-quency domain.The experimental results show that the reconstructed wideband speech generated by the proposed method is superior to the traditional heuris-tic rule based approaches and the conventional neu-ral network methods for both subjective and objective evaluation.

关 键 词：speech bandwidth extension temporal convolutional networks time-frequency loss 

分 类 号：TN912.3[电子电信—通信与信息系统] TP183[电子电信—信息与通信工程]