基於資訊最大化及時頻聚類之盲訊號源分離超大型積體電路架構設計

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姓名

陳映全(YING-CHUAN CHEN) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

基於資訊最大化及時頻聚類之盲訊號源分離超大型積體電路架構設計
(VLSI Architecture Design for Blind Source Separation based on Infomax and Time-frequency Masking)

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摘要(中)

盲訊號分離的研究可以分為即時混合(Instantaneous Mixture)和旋積混合(Convolutive Mixtures)，在即時混合的研究上，已有不錯的成果，因為解混合矩陣在訓練係數過程中需要很大的運算量，所以將這個部分實現在超大型積體電路(Very-large-scale integration , VLSI)是一個不錯的選擇，本論文想在解旋積混合的硬體做一些改進，希望可以加快處理速度，應付未來應用上之需要。我們主要採取的演算法有兩種並將其實現為超大型積體電路，其中第一種為Infomax演算法，我們使用Torkkola所提出的架構來實現旋積盲訊號源分離，Torkkola的學習規則近似於最小均方誤差演算法，所以我們利用近似於最小均方誤差適應性濾波器的延遲最小均方誤差適應性濾波器來做修改應用到旋積盲訊號源分離中；而第二種為時頻聚類之盲訊號源分離，主要是將訊號做特徵擷取後再經過聚類演算法來達到訊號分離的效果，在這我們提出了特徵節取和K-means的硬體架構。此外我們利用了壓縮感測來增強和重建其分離的訊號，最後也提出了壓縮感測中Orthogonal Matching Pursuit的硬體架構。

摘要(英)

Blind source separation (BSS) of independent sources from their convolutive mixtures is a problem in many real world applications. In this paper, we design two VLSI architectures for convolutive BSS (CBSS). The first is based on Infomax algorithm and the BSS structure proposed by Torkkola is utilized. As its learning rule is similar to least mean squares (LMS), we apply delayed LMS (DLMS) to BSS. The proposed architecture based on sharing multiplication improves adaptation delays and critical path. The second VLSI architecture is based on time-frequency masking based BSS. This method generates useful features for each time-frequency points and then clusters them to achieve signal separation. For this algorithm, we propose VLSI modules for feature generation and K-means and orthogonal matching pursuit.

關鍵字(中)

★ 適應性濾波器
★ 盲源分離
★ 資訊最大化
★ 壓縮感測

關鍵字(英)

★ Blind Source Separation
★ adaptive filter
★ Orthogonal Matching Pursuit
★ Infomax

論文目次

摘要...........................................................................................................................................ii
Abstract……………………………………………………………………………………....iii
圖目錄…………………………………………………………………………………………iv
表目錄 …………………………………………………………………………………….vi
章節目次 vii
第一章緒論 1
1.1 前言 1
1.2 研究動機與目的 1
1.3 論文架構 2
第二章盲訊號源分離簡介 4
2.1 簡介(Introduction) 4
2.2 混合模型(Mixing Model) 4
2.2.1旋積混合模型(Convolutive Mixtures Model) 4
2.2.2即時混合模型(Instantaneous Mixing Model) 5
2.2.3在頻率域上的旋積混合 6
2.3 Over and Under-determined 6
2.4 分離模型(Separation Model) 7
2.4.1 Feed-forward Structure 8
2.4.2 Feedback Structure 9
2.4.3 兩個輸入兩個輸出系統 10
2.5 分離原理 11
2.5.1 Independent Component Analysis(ICA) and BSS 11
2.6 盲訊號源分離(Blind source separation)硬體架構相關研究 12
第三章基於Infomax盲訊號源分離之VLSI架構 13
3.1 Information Theoretic 13
3.2延遲最小均方演算法(Delayed Least Mean Squares) 17
3.3延遲最小均方演算法之硬體架構 19
3.3.1 Proposed design for enhance DLMS architecture 21
3.5從Infomax解BSS之演算法到VLSI架構 26
第四章時頻聚類盲訊號源分離之VLSI架構 34
4.1 概觀時頻聚類盲訊號源分離 34
4.2 特徵參數選取與其VLSI架構 36
4.2.1 特徵參數選取演算法 36
4.2.2 特徵參數選的VLSI架構 37
4.3 K-Means分群演算法與其VLSI架構 38
4.3.1 可硬體式K-Means演算法 39
4.3.2 K-Means 硬體架構 40
4.4 壓縮感測與其VLSI架構 45
4.4.1壓縮感測演算法 45
4.4.2 OMP(Orthogonal Matching Pursuit)硬體架構 47
第五章實驗結果 61
5.1 基於infomax盲訊號源分離之VLSI架構 61
5.2 時頻聚類之盲訊號源分離之VLSI架構 65
第六章結論及未來研究方向 69
參考文獻 71

參考文獻

[1] Hyvarinen, E. Oja, Independent component analysis: Algorithms and applications, Neural Networks 13 (2000) 411–430.
[2] S. Roberts and R. Everson, Independent component analysis : Principles and Practice., Cambridge University Press, 2001.
[3] S. C. Douglas, Malay Gupta, Hiroshi Sawada, and Shoji Makino, “Spatio-Temporal FastICA Algorithms for the Blind Separation of Convolutive Mixtures,” IEEE Transactions Audio, Speech and Language Processing, Vol. 15, No. 5, pp. 204–215, 2007.
[4] H. Saruwatari, T. Kawamura, T. Nishikawa, A. Lee, and K. Shikano,“Blind source separation based on a fast-convergence algorithm combining ICA and beamforming,” IEEE Trans. Audio, Speech, Lang.Process., vol. 14, no. 2, pp. 666–678, Mar. 2006.
[5] A. Belouchrani, and M. G. Amin, “Blind source separation based on time-frequency signal representations”, IEEE Trans. on Signal Processing, vol.46(11), pp.2888-2897, Nov. 1998.
[6] S. Winter, W. Kellermann, H. Sawada, and S. Makino,“MAP-based underdetermined blind source separation of convolutive mixtures by hierarchical clustering and ℓ1- norm minimization,” EURASIP Journal on Advances in Signal Processing, vol. 2007, 2007, article ID 24717.
[7] P. Boﬁll, “Underdetermined blind separation of delayed sound sources in the frequency domain,” Neurocomputing, vol. 55, pp. 627–641, 2003.
[8] P. Bofill and M. Zibulevsky, “Underdetermined blind source separation using sparse representations,” Signal Process., vol. 81, pp. 2353 – 2362, Jun. 2001.
[9] Y. Li, S. Amari, A. Cichocki, D. W. C. Ho, and S. Xie, “Underdetermined blind source separation based on sparse representation,” IEEETrans. Signal Process., vol. 54, no. 2, pp. 423–437, Feb. 2006.
[10] A. Aissa-El-Bey, K. Abed-Mraim and Y. Grenier, “Blind separation of underdetermined convolutive mixtures using their time-frequency representation,” IEEE Trans. Audio, Speech, Lang. Process., vol. 15, pp. 1540 – 1550, Jul. 2007.
[11] A.J. Bell, T.J. Sejnowski, “Blind separation and blind deconvolution: an information-theoretic approach,” 1995 International Conference on Acoustics, Speech, and Signal Processing, 1995. ICASSP-95., vol.5, no., pp.3415-3418 vol.5, 9-12 May. 1995.
[12] K.S. Cho, S.Y. Lee, “Analog CMOS implementation of nonholonomic ICA algorithm with automatic offset compensation,” 2003. Proceedings of the 2003 International Conference on Neural Networks and Signal Processing, vol.1, no., pp. 279- 282 vol.1, 14-17 Dec. 2003.
[13] M. Ounas, R. Touhami, M.C.E. Yagoub, “Low Cost Architecture of Digital Circuit for FPGA Implementation Based ICA Training Algorithm of Blind Signal Separation,” 2007. ISSSE ’’07. International Symposium on Signals, Systems and Electronics, vol., no., pp.135-138, July 30 2007-Aug. 2 2007.
[14] Z. Li, Q. Lin, “FPGA Implementation of Infomax BSS Algorithm with Fixed-Point Number Representation,” International Conference on Neural Networks and Brain, 2005. ICNN&B ’’05., vol.2, no., pp.889-892, 13-15 Oct. 2005.
[15] A.B. Lim, J.C. Rajapakse, A.R. Omondi, “Comparative study of implementing ICNNs on FPGAs,” International Joint Conference on Neural Networks, 2001. Proceedings. IJCNN ’’01., vol.1, no., pp.177-182 vol.1, 2001.
[16] M. Ounas, S. Chitroub, R. Touhami, M. Yagoub, S. Gaoua, “Digital circuit design for FPGA based implementation of ICA for real time Blind Signal Separation,” International Conference on Microelectronics, 2008. ICM 2008., vol., no., pp.60-63, 14-17 Dec. 2008.
[17] H. Herzberg, R. Haimi-Cohen, “A systolic array realization of an LMS adaptive filter and the effects of delayed adaptation,” IEEE Transactions on Signal Processing, vol. 40, no.11, pp.2799-2803, Nov. 1992.
[18] F. Sattar, C. Charayaphan, “Low-cost design and implementation of an ICA-based blind source separation algorithm,” 2002. 15th Annual IEEE International ASIC/SOC Conference, vol., no., pp. 15- 19, 25-28 Sept. 2002.
[19] C. Charoensak, F. Sattar, “A single-chip FPGA design for real-time ICA-based blind source separation algorithm,” IEEE International Symposium on Circuits and Systems, 2005. ISCAS 2005., vol., no., pp. 5822- 5825 Vol. 6, 23-26 May. 2005.
[20] C. Charoensak, F. Sattar, “System-level design of low-cost FPGA hardware for real-time ICA-based blind source separation,” Proceedings. IEEE International SOC Conference, 2004., vol., no., pp. 139- 140, 12-15 Sept. 2004.
[21] Aapo Hyvarinen, Juha Karhunen, and Erkki Oja, Independent Component Analysis,John Wiley,2001.
[22] K. Torkkola, “Blind separation of delayed and convolved sources,” In S. Haykin, editor, Unsupervised Adaptive Filtering, Vol. I, pages 321-375. Wiley, 2000.
[23] J. Xi, J.P. Reilly, “Blind separation and restoration of signals mixed in convolutive environment,” 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, 1997. ICASSP-97., vol.2, no., pp.1327-1330 vol.2, 21-24 Apr. 1997.
[24] J.P. Reilly, L.C. Mendoza, “Blind signal separation for convolutive mixing environments using spatial-temporal processing,” 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing, 1999. ICASSP ’’99. Proceedings., vol.3, no., pp.1437-1440 vol.3, 15-19 Mar. 1999.
[25] G. Long, F. Ling, J.G. Proakis, “The LMS algorithm with delayed coefficient adaptation,” IEEE Transactions on Acoustics, Speech and Signal Processing, vol. 37, no.9, pp.1397-1405, Sep. 1989.
[26] G. Long, F. Ling, J.G. Proakis, “Corrections to “The LMS algorithm with delayed coefficient adaptation,” IEEE Transactions on Signal Processing, vol. 40, no.1, pp.230-232, Jan. 1992.
[27] L.D. Van, W.S. Feng, “An efficient systolic architecture for the DLMS adaptive filter and its applications,” IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, vol.48, no.4, pp.359-366, Apr. 2001.
[28] J. Park, K. Muhammad, K. Roy, “High-performance FIR filter design based on sharing multiplication,” IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol.11, no.2, pp.244-253, April. 2003.
[29] M.D. Meyer, D.P. Agrawal, “A high sampling rate delayed LMS filter architecture,” IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, vol. 40, no.11, pp.727-729, Nov. 1993.
[30] V. G. Reju, S. N. Koh and I. Y. Soon, “Underdetermined convolutive blind source separation via time-frequency masking,” IEEE Trans. Audio, Speech, Lang. Process., vol. 18, pp. 101 – 116, Jan. 2010.
[31] S. Araki, H. Sawada, R. Mukai and S. Makino, “Underdetermined blind sparse source separation for arbitrarily arranged multiple sensors,” Signal Process., vol. 87, pp. 1833 – 1847, Feb. 2007.
[32] R. Lyons, “Another contender in the arctangent race," IEEE Signal Processing Magazine,vol.21, no.1, pp. 109-110, Jan. 2004.
[33] Hu, Y.H.; , "CORDIC-based VLSI architectures for digital signal processing," Signal Processing Magazine, IEEE , vol.9, no.3, pp.16-35, July 1992.
[34] T.-W. Chen and S.-Y. Chien, “Bandwidth adaptive hardware architecture of K-means clustering for video analysis,” IEEE Trans. Very Large Scale Integr. (VLSI) Syst., vol. 18, no. 6, pp. 957–966, Jun. 2010.
[35] K. S. Gurumoorthy, A. Rajwade, A. Banerjee, and A. Rangarajan, “A method for compact image representation using sparse matrix and tensor projections onto exemplar orthonormal bases,” IEEE Trans. Image Process., vol. 19, no. 2, pp. 322-334, Feb. 2010.
[36] J. C. Yang, J. Wright , T. S. Huang, and Y. Ma, “Image super-resolution via sparse representation,” IEEE Trans. Image Process., vol. 19, no. 11, pp. 2861-2873, Nov. 2010.
[37] W. S. Dong, L. Zhang, G. M. Shi, and X. L. Wu, “Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization,” IEEE Trans. Image Process., vol. 20, no. 7, pp. 1838-1857, July 2011.
[38] Y. Li, S.-I Amari, A. Cichocki, and C. Guan, “Probability estimation for recoverability analysis of blind source separation based on sparse representation,” IEEE Trans. Inf. Theory, vol. 52, no. 7, pp. 3139-3152, July 2006.
[39] M. Elad and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Trans. Image Process., vol. 15, no. 12, pp. 3736-3745, Dec. 2006.
[40] M. Lustig, D. L. Donoho, J. M. Santos, and J. M. Pauly, “Compressed sensing MRI,” IEEE Signal Processing Mag., vol. 25, no. 2, pp. 72-82, Mar. 2008.
[41] M. Marim, E. Angelin, and J.-C. Olivo-Marin, “A compressed sensing approach for biological microscopic image processing,” IEEE Int. Symp. Biomedical Imaging, 2009, pp. 1374-1377.
[42] L. Zhu, Y. L. Zhu, H. Mao, and M. H. Gu, “A new method for sparse signal denoising based on compressed sensing,” Int. Symp. Knowledge Acquisition and Modeling, 2009, pp. 35-38.
[43] J. J. Han, O. Loffeld, K. Hartmann, and R.Wang, “Multi image fusion based on compressive sensing,” Int. Conf. Audio Language and Image Processing, 2010, pp. 1463-1469.
[44] J. Wu, F. Liu, L. C. Jiao, and X. D. Wang, “Compressive sensing SAR image reconstruction based on Bayesian framework and evolutionary computation,” IEEE Trans. Image Process., vol. 20, no. 7, pp. 1904-1911, July 2011.
[45] N. Yu, T. Qiu, F. Bi, and A. Wang, “Image features extraction and fusion based on joint sparse representation,” IEEE J. Selected Topics Signal Process., vol.5, no. 5, pp. 1074-1082, Sept. 2011.
[46] J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 2, pp. 210–227, Feb. 2008.
[47] M. Aharon, M. Elad, and A. Bruckstein, “K-SVD: an algorithm for designing overcomplete dictionaries for Sparse Representation,” IEEE Trans. Signal Process., vol. 54, no. 11, pp. 4311–4322, Nov. 2006.
[48] S. G. Mallat and Z. F. Zhang, “Matching pursuits with time-frequency dictionaries,” IEEE Trans. Signal Process., vol. 41, no. 12, pp. 3397-3415, Dec. 1993.
[49] J. A. Tropp and A. C. Gilbert, “Signal recovery from random measurements via orthogonal matching pursuit,” IEEE Trans. Inf. Theory, vol. 53, pp. 4655-4666, 2007.
[50] D. Needell and R. Vershynin, “Signal recovery from incomplete and inaccurate measurements via regularized orthogonal matching pursuit,” IEEE J. Selected Topics Signal Process., vol. 4, no. 2, pp. 310-316, Apr. 2010
[51] P. B.Denyer and D. Renshaw, “VLSI Signal Processing; A Bit-Serial Approach,"Addison-Wesley Longman Publishing Co., Boston, MA, USA, 1985

指導教授

王家慶(JIA-CHING WANG)

審核日期

2012-8-7

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