The　bandwidth　limitation　of　wideband　audio　degrades　the　subjective　quality　and　the　naturalness.　In　this　paper,　a　bandwidth　extension　of　audio　signals　from　wideband　to　super-wideband　was　proposed　by　using　a　similarity　correlation　degree-based　neural　network.　Firstly,　the　fine　spectrum　of　wideband　audio　was　converted　to　a　multi-dimensional　phase　space.　Then,　a　similarity　correlation　degree-based　neural　network　was　built　up　to　reproduce　the　high-frequency　fine　spectrum.　In　addition,　Gaussian　mixture　model　was　used　to　estimate　the　high-frequency　spectral　envelope.　Finally,　the　bandwidth　was　extended　to　super-wideband　by　the　proposed　method　in　the　ITU-T　G.　722.　1　wideband　codec.　Evaluation　results　indicate　that　the　proposed　method　is　preferred　over　the　reference　methods　and　achieves　a　comparable　subjective　quality　with　the　G.　722.　1C　super-wideband　codec.　©,　2015,　Chinese　Institute　of　Electronics.　All　right　reserved.