基于Takenaka-Malmquist系的语音信号压缩与降噪方法

doi:10.12066/j.issn.1007-2861.1996

Abstract

Abstract:

The sparse representation of speech signal is one of the important research directions in speech compression, denoising and other speech processing. On the basis of matching pursuit (MP), orthogonal matching pursuit (OMP) and other greedy algorithms, this paper proposes a greedy weight algorithm based on Takenaka-Malmquist system (TMGW) for the compression of speech signal. This algorithm has the advantage of requiring only fewer decomposition numbers when reconstructing the speech signal, and it does well for achieving the goal of speech compression. Besides, in view of the fact that energy distribution between the signal and noise at time-frequency surface after sparse decomposition is different, this algorithm can realize the purpose of denoising. The experiment results show that the TMGW algorithm is more effective for the sparse representation of speech signal than the matching pursuit algorithm based on the adaptive Gabor sub-dictionary (GMP).

Key words: greedy weight algorithm based on the Takenaka-Malmquist system (TMGW), sparse representation, voice compression, voice denoising

CLC Number:

TN912

Ya LEI, Yong FANG, Liming ZHANG. Research on compression and denoising of speech signal based on the Takenaka-Malmquist system[J]. Journal of Shanghai University（Natural Science Edition）, 2020, 26(1): 33-46.

Figures/Tables 17

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Fig. 10

Fig. 11

Fig. 12

Fig. 13

Fig. 14

Table 1

Table 2

Fig. 15

References 22

[1]	曹洁 . 基于DSP的语音信号滤波技术研究[D]. 兰州: 兰州理工大学, 2011: 1-3.
	Cao J . Research on DSP based voice signal filtering technology[D]. Lanzhou: Lanzhou University of Technology, 2011: 1-3.
[2]	郭金库 . 信号稀疏表示理论及其应用 [M]. 北京: 科学出版社, 2013: 20-53.
[3]	Rudin W . Function theory in the unit ball of ${C}^N$[M]. Berlin: Springer, 1980: 1-50.
[4]	Martínez-Avendaño R A, Rosenthal P . An introduction to operators on the Hardy-Hilbert space[M]. Berlin: Springer, 2006: 1-34.
[5]	吴勃英, 林迎珍 . 应用型再生核空间 [M]. 北京: 科学出版社, 2012: 50-100.
[6]	Qian T, Wang Y B . Adaptive Fourier series: a variation of greedy algorithm[J]. Advances in Computational Mathematics, 2011,34(3):279-293.
[7]	Qian T . Adaptive Fourier decompositions and rational approximations, part Ⅰ: theory[J]. International Journal of Wavelets Multiresolution and Information Processing, 2014,12(5):54-67.
[8]	钱涛 . 自适应Fourier变换: 一个贯穿复几何, 调和分析及信号分析的数学方法 [M]. 北京: 科学出版社, 2015: 1-200.
[9]	Zhang L M, Hong W, Mai W X , et al. Adaptive Fourier decomposition and rational approximation, part Ⅱ: software system design and development[J]. International Journal of Wavelets Multiresolution and Information Processing, 2014,12(5):413-423.
[10]	Mallat S G, Zhang Z . Matching pursuits with time-frequency dictionaries[J]. IEEE Transactions on Signal Processing, 1993,41(12):3397-3415.
[11]	Chang L H, Wu J Y . An improved RIP-based performance guarantee for sparse signal recovery via orthogonal matching pursuit[J]. IEEE Transactions on Information Theory, 2014,60(9):405-408.
[12]	Qian T, Zhang L M, Li Z X . Algorithm of adaptive Fourier decomposition[J]. IEEE Transactions on Signal Processing, 2011,59(12):5899-5906.
[13]	He C, Zhang L M, He X J , et al. A new image decomposition and reconstruction approach: adaptive Fourier decomposition [C]// International Conference on Multimedia Modeling. 2015: 227-236.
[14]	Wang Z, Wan F, Wong C M , et al. Adaptive Fourier decomposition based ECG denoising[J]. Computers in Biology and Medicine, 2016,77:195-205.
[15]	Zhang L M . Adaptive Fourier decomposition based time-frequency analysis[J]. Journal of Electronic Science and Technology, 2014,12(2):201-205.
[16]	陈丹艳 . 自适应啸叫抑制算法的研究与DSP实现[D]. 成都: 电子科技大学, 2014: 18-19.
	Chen D Y . Research on adaptive squeal suppression algorithm and DSP implementation[D]. Chengdu: University of Electronic Science and Technology, 2014: 18-19.
[17]	Kobayashi Y, Kondo K . Speech intelligibility estimation using support vector regression and critical band segmental SNR in noisy condition[J]. IEEJ Transactions on Electronics, Information and Systems, 2013,133(8):1556-1564.
[18]	Gao Y, Ku M, Qian T , et al. FFT formulations of adaptive Fourier decomposition[J]. Journal of Computational and Applied Mathematics, 2017,324:204-215.
[19]	尹忠科, 邵君, Vandergheynst P . 利用FFT实现基于MP的信号稀疏分解[J]. 电子与信息学报, 2006,28(4):614-618.
	Yin Z K, Shao J, Vandergheynst P . Signal sparse decomposition based on MP using FFT[J]. Journal of Electronics and Information, 2006,28(4):614-618.
[20]	尹忠科, 王建英, 邵君 . 基于原子库结构特性的信号稀疏分解[J]. 西南交通大学学报, 2005,40(2):173-178.
	Yin Z K, Wang J Y, Shao J . Signal sparse decomposition based on the structural characteristics of atomic database[J]. Journal of Southwest Jiaotong University, 2005,40(2):173-178.
[21]	Cen Y G, Wang F F, Zhao R Z , et al. Tree-based backtracking orthogonal matching pursuit for sparse signal reconstruction[J]. Journal of Applied Mathematics, 2013,2013:1-8.
[22]	王军华, 方勇 . 基于Curvelet域自适应数学形态学降噪的含噪图像盲分离方法[J]. 上海大学学报(自然科学版), 2010,16(4):336-341.
	Wang J H, Fang Y . A blind image separation method based on adaptive mathematical morphology noise reduction in curvelet domain[J]. Journal of Shanghai University (Natural Science Edition), 2010,16(4):336-341.

Research on compression and denoising of speech signal based on the Takenaka-Malmquist system

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 17

References 22

Related Articles 1

Recommended Articles

Metrics

Comments