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.