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A new source‐filter model audio bandwidth extension using high frequency perception feature for IoT communications
Author(s) -
Jiang Lin,
Yu Shaoqian,
Wang Xiaochen,
Wang Chao,
Wang Tonghan
Publication year - 2018
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.4638
Subject(s) - computer science , bandwidth extension , codec , crest factor , speech recognition , coding (social sciences) , sub band coding , sound quality , perception , speech coding , audio signal , adaptive multi rate audio codec , bandwidth (computing) , voice activity detection , telecommunications , speech processing , mathematics , statistics , neuroscience , biology
Summary Audio coding is a generic technology to IoT applications system. Audio bandwidth extension is a standard technique within contemporary audio codecs to efficiently code audio signals at low bitrates. In existing methods, high frequency signal is generated by a duplication of the corresponding low frequency and some parameters of high frequency. However, the perception quality of coding will significantly degrade if the correlation between high and low frequency becomes weak. In this paper, we proposed a new source‐filter model audio bandwidth extension method. In our method, a perception feature of the high frequency signal is extracted to restore the perception quality of coding. In the decoder side, the crest factor and noise level are obtained under the constraint of the high frequency perception parameter. The performance shown in our experiment results are superior to the classic methods. Compared with the state of art method, the proposed method is also comparative because of the coding bitrate is significantly reduced and keeps a close perception quality of coding. This paper also provided a new solution for IoT communications that requires low bitrates and high quality of coding, especially like Internet of Vehicles.