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Author:

Bai, Hai-Chuan (Bai, Hai-Chuan.) | Bao, Chang-Chun (Bao, Chang-Chun.) (Scholars:鲍长春) | Liu, Xin (Liu, Xin.)

Indexed by:

EI Scopus PKU CSCD

Abstract:

The auditory quality of wideband audio is generally degraded due to the lack of the high-frequency in network transmission, so this paper presents a kind of audio bandwidth extension method from wideband to super wideband based on local least square support vector machine. In the light of the nonlinearity of audio spectrum, the high-frequency fine spectrum of audio signals is predicted by using phase space reconstruction and local least square support vector machine. Combining with the estimation of high-frequency sub-band energy based on Gaussian mixture model, the proposed method can effectively recover the high-frequency components in the frequency range 7 kHz~14 kHz through the envelope adjustment of high-frequency spectrum at last. Subjective and objective testing results indicate that the proposed method improves the auditory quality of wideband audio and outperforms the reference methods of audio bandwidth extension. © 2016, Chinese Institute of Electronics. All right reserved.

Keyword:

Frequency estimation Gaussian distribution Support vector machines Bandwidth Phase space methods Subjective testing Least squares approximations Vector spaces Vectors

Author Community:

  • [ 1 ] [Bai, Hai-Chuan]School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing; 100124, China
  • [ 2 ] [Bao, Chang-Chun]School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing; 100124, China
  • [ 3 ] [Liu, Xin]School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing; 100124, China

Reprint Author's Address:

  • [liu, xin]school of electronic information and control engineering, beijing university of technology, beijing; 100124, china

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Source :

Acta Electronica Sinica

ISSN: 0372-2112

Year: 2016

Issue: 9

Volume: 44

Page: 2203-2210

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 2

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 5

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