基于相似关联度神经网络的音频频带扩展

Audio Bandwidth Extension Method Using Similarity Correlation Degree-Based Neural Network

宽带音频带宽的限制会降低其主观质量和自然度.本文提出了一种基于相似关联度神经网络的宽带向超宽带音频频带扩展方法.该方法将宽带音频的精细谱重构成多维相空间,并建立相似关联度神经网络来恢复高频成分的精细谱,同时借助高斯混合模型估计高频谱包络,并以G.722.1编码器为平台实现音频信号的带宽扩展.测试结果表明,本文方法扩展性能优于参考方法,其主观质量接近于G.722.1C超宽带编码器.

The bandwidth limitation of wideband audio degrades the subjective quality and the naturalness. In this paper, a bandwidth extension of audio signals from wideband to super-wideband was proposed by using a similarity correlation degree-based neural network. Firstly, the fine specmma of wideband audio was converted to a multi-dimensional phase space. Then, a similarity correlation degree-based neural network was built up to reproduce the high-frequency fine spectrum. In addition, Gaussian mixture model was used to estimate the high-frequency spectral envelope. Finally, the bandwidth was extended to super-wideband by the proposed method in the ITU-T G. 722.1 wideband codec. Evaluation results indicate that the proposed method is preferred over the reference methods and achieves a comparable subjective quality with the G. 722.1C super-wideband codec.