Audio bandwidth extension using ensemble of recurrent neural networks

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