Artificial bandwidth extension using deep neural network-based spectral envelope estimation and enhanced excitation estimation

• Published in: IET signal processing 2016-06, Vol.10 (4), p.422-427
• Main Authors: Li, Yaxing, Kang, Sangwon
• Format: Article
• Language: English
• Subjects: adaptive multirate codec; adaptive spectral double shifting method; artificial bandwidth extension; Bandwidth; deep neural network‐based spectral envelope estimation; Distortion; enhanced excitation estimation; enhanced spectrum envelope; Envelopes; Excitation; excitation estimation; filtering theory; log spectral distortion; Narrowband; narrowband speech signal quality; neural nets; Neural networks; perceptual evaluation; Spectra; Speech; speech coding; speech synthesis; whitening filter
• ISSN: 1751-9675; 1751-9683; 1751-9683
• DOI: 10.1049/iet-spr.2015.0375

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.