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Author:

Liu, Haojie (Liu, Haojie.) | Bao, Changchun (Bao, Changchun.) (Scholars:鲍长春) | Liu, Xin (Liu, Xin.) | Zhang, Xingtao (Zhang, Xingtao.) | Zhang, Liyan (Zhang, Liyan.)

Indexed by:

CPCI-S

Abstract:

In this paper a new method of blind bandwidth extension from wideband (WB) to super-wideband (SWB) audio is proposed. The Radial Basic Function (RBF) neural network is utilized to predict the coefficients of high-frequency (HF) based on the nonlinear characteristics of audio spectrum series. In addition, the linear extrapolation is used for reconstructing the envelop of HF spectrum. The bandwidth of the reconstructed audio signals is extended to SWB by using the proposed method. The result of the objective performance evaluation indicates that the proposed method can reconstruct the truncated HF components effectively and outperforms the conventional algorithms of blind bandwidth extension.

Keyword:

linear extrapolation Bandwidth extension RBF neural network

Author Community:

  • [ 1 ] [Liu, Haojie]Beijing Univ Technol, Sch Elect Informat & Control Engn, Speech & Audio Signal Proc Lab, Beijing 100124, Peoples R China
  • [ 2 ] [Bao, Changchun]Beijing Univ Technol, Sch Elect Informat & Control Engn, Speech & Audio Signal Proc Lab, Beijing 100124, Peoples R China
  • [ 3 ] [Liu, Xin]Beijing Univ Technol, Sch Elect Informat & Control Engn, Speech & Audio Signal Proc Lab, Beijing 100124, Peoples R China
  • [ 4 ] [Zhang, Xingtao]Beijing Univ Technol, Sch Elect Informat & Control Engn, Speech & Audio Signal Proc Lab, Beijing 100124, Peoples R China
  • [ 5 ] [Zhang, Liyan]Beijing Univ Technol, Sch Elect Informat & Control Engn, Speech & Audio Signal Proc Lab, Beijing 100124, Peoples R China

Reprint Author's Address:

  • [Liu, Haojie]Beijing Univ Technol, Sch Elect Informat & Control Engn, Speech & Audio Signal Proc Lab, Beijing 100124, Peoples R China

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Source :

2011 IEEE INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND INFORMATION TECHNOLOGY (ISSPIT)

ISSN: 2162-7843

Year: 2011

Page: 150-154

Language: English

Cited Count:

WoS CC Cited Count: 4

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 11

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