Open AccessArtificial bandwidth extension using deep neural network‐based spectral envelope estimation and enhanced excitation estimation
Author(s) -
Li Yaxing,
Kang Sangwon
Publication year - 2016
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
ISSN - 1751-9683
DOI - 10.1049/iet-spr.2015.0375
Subject(s) - spectral envelope , computer science , narrowband , bandwidth extension , bandwidth (computing) , artificial neural network , speech recognition , artificial intelligence , speech coding , telecommunications , audio signal
The authors propose a robust artificial bandwidth extension (ABE) technique to improve narrowband (NB) speech signal quality using an enhanced spectrum envelope and excitation estimation. For envelope estimation, they propose an enhanced envelope estimation method using a deep neural network with multiple layers. For excitation estimation, they use a whitened NB excitation signal that is generated by passing the excitation signal through a whitening filter. An adaptive spectral double shifting method is introduced to obtain an enhanced wideband (WB) excitation signal. The proposed ABE system is applied to the decoded output of an adaptive multi‐rate (AMR) codec at 12.2 kbps. They evaluate its performance using log spectral distortion, a WB perceptual evaluation of speech quality, and a formal listening test. The objective and subjective evaluations confirm that the proposed ABE system provides better speech quality than AMR at the same bit rate.