Compressive Sensing Framework for Speech Signal Synthesis Using a Hybrid Dictionary
Compressive sensing (CS) is a promising focus in signal processing field, which offers a novel view of simultaneous compression and sampling. In this framework a sparse approximated signal is obtained with samples much less than that required by the Nyquist sampling theorem if the signal is sparse on one basis. Encouraged by its exciting potential application in signal compression, we use CS framework for speech synthesis problems. The linear prediction coding (LPC) is an efficient tool for speech compression, as the speech is considered to be an AR process. It is also known that a speech signal is quasi-periodic in its voiced parts, hence a discrete fourier transform (DFT) basis will provide a better approximation. Thus we propose a hybrid dictionary combined with the LPC model and the DFT model as the basis of speech signal. The orthogonal matching pursuit (OMP) is employed in our simulations to compute the sparse representation in the hybrid dictionary domain. The results indicate good performance with our proposed scheme, offering a satisfactory perceptual quality.
speech synthesis compressive sensing linear prediction coding DFT basis
Yue Wang Zhixing Xu Gang Li Liping Chang Chuanrong Hong
College of Information Engineering Zhejiang University of Technology 310014 Hangzhou, Zhejiang, P.R.China
国际会议
2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)
上海
英文
2426-2429
2011-10-15（万方平台首次上网日期，不代表论文的发表时间）