In audio communication systems, the perceptual audio quality of the reproduced audio signals such as the\nnaturalness of the sound is limited by the available audio bandwidth. In this paper, a wideband to super-wideband\naudio bandwidth extension method is proposed using an ensemble of recurrent neural networks. The feature space\nof wideband audio is firstly divided into different regions through clustering. For each region in the feature space, a\nspecific recurrent neural network with a sparsely connected hidden layer, referred as the echo state network, is\nemployed to dynamically model the mapping relationship between wideband audio features and high-frequency\nspectral envelope. In the following step, the outputs of multiple echo state networks are weighted and fused by\nmeans of network ensemble, in order to further estimate the high-frequency spectral envelope. Finally, combining\nthe high-frequency fine spectrum extended by spectral translation, the proposed method can effectively extend the\nbandwidth of wideband audio to super wideband. Objective evaluation results show that the proposed method\noutperforms the hidden Markov model-based bandwidth extension method on the average in terms of both static\nand dynamic distortions. In subjective listening tests, the results indicate that the proposed method is able to\nimprove the auditory quality of the wideband audio signals and outperforms the reference method.
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