Temporal convolutional network for speech bandwidth extension

In the field of speech bandwidth extension, 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 complexity 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 enable narrowband speech to acquire a more accurate wideband mapping in the time domain and the frequency domain. The experimental results show that the reconstructed wideband speech generated by the proposed method is superior to the traditional heuristic rule based approaches and the conventional neural network methods for both subjective and objective evaluation.