A Novel Research to Artificial Bandwidth Extension Based on Deep BLSTM Recurrent Neural Networks and Exemplar-Based Sparse Representation

This paper presents a two stages artificial bandwidth extension (ABE) framework which combine deep bidirectional Long Short Term Memory (BLSTM) recurrent neural network with exemplar-based sparse representation to estimate missing frequency band. It demonstrates the suitability of proposed method for modeling log power spectra of speech signals in ABE. The BLSTM-RNN which can capture information from anywhere in the feature sequence is used to estimate the log power spectra in the high-band firstly and the exemplar-based sparse representation which could alleviate the over-smoothing problem is applied to generated log power spectra in the second stage. In addition, rich acoustic features in the low-band are considered to reduce the reconstruction error. Experimental results demonstrate that the proposed framework can achieve significant improvements in both objective and subjective measures over the different baseline methods.