A DNN regression approach to speech enhancement by artificial bandwidth extension

Artificial speech bandwidth extension (ABE) is an extremely effective means for speech enhancement at the receiver side of a narrowband telephony call. First approaches have been seen incorporating deep neural networks (DNNs) into the estimation of the upper band speech representation. In this paper we propose a regression-based DNN ABE being trained and tested on acoustically different speech databases, exceeding coded narrowband speech by a so-far unseen 1.37 CMOS points in a subjective listening test.

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