东南大学学报（自然科学版）2012，Vol.42Issue(6)：1027-1030,4.DOI:10.3969/j.issn.1001-0505.2012.06.001
自适应语音压缩感知方法
Adaptive compressed sensing method for speech
摘要
Abstract
To overcome the problem that the method of sparsification for speech signal based on fixed orthogonal base has a low sparsity and is not adaptive, a new adaptive sparsification algorithm is developed for speech signal compression. First, speech signal is predicted by linear predication using weighted linear combination of linear predictive coefficients, and the linear prediction residual are used as the signal bases. Then, the adaptive training dictionary is trained under the sparsity constraint, and the dictionary and sparsity coefficients are updated by alternatively using 1-norm sparsity constraint pursuit and singular value decomposition (SVD) algorithm. By analyzing the feature of speech signals, the new scheme can exactly extract essential feature or texture feature, and can obtain better sparsification performance and reconstruction performance for speech signal. The experimental results show that compared with other orthogonal base algorithms, the sparsity of speech signals with the proposed method is obviously improved. The subjective and objective evaluation results of speech quality also show that the proposed method exhibits a good reconstruction performance in speech signal.关键词
压缩感知/稀疏性/语音/线性预测Key words
compressed sensing/ sparsity/ speech/ linear prediction分类
信息技术与安全科学引用本文复制引用
罗武骏,陶文凤,左加阔,赵力..自适应语音压缩感知方法[J].东南大学学报（自然科学版）,2012,42(6):1027-1030,4.基金项目
国家自然科学基金资助项目(51075068,61201326,61231002,61273266)、教育部博士点基金资助项目(20110092130004)、江苏省高校自然科学研究基金资助项目(12KJB510021). （51075068,61201326,61231002,61273266）
