參考文獻 |
[1] I. B. Thomas and A. Ravindran, “Intelligibility enhancement of already noisy speech signals,” J. Audio Eng. Soc., vol. 22, pp. 234-236, May 1974.
[2] J. F. Gemmeke, H. V. Hamme, B. Cranen, and L. Boves, “Compressive sensing for missing data imputation in noise robust speech recognition,” IEEE J. Sel. Topics Signal Process., vol. 4, no.2, Apr. 2010.
[3] 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, Nov, 2006.
[4] S. T. Roweis, “Factorial models and refiltering for speech separation and denoising,” Interspeech 2003.
[5] M. Cooke, P. Green, L. Josifovski, and A. Vizinho, “Robust automatic speech recognition with missing and unreliable acoustic data,” Speech Commun., vol. 34, pp. 267–285, 2001.
[6] L. Josifovski, M. Cooke, P. Green, and A. Vizinho, “State based imputation of missing data for robust speech recognition and speech enhancement,” in Proc. Eurospeech, 1999, pp. 2837–2840.
[7] J. F. Gemmeke and B. Cranen, “Using sparse representations for missing data imputation in noise robust speech recognition,” in Proc. EUSIPCO, 2008.
[8] D. Lee and H. Seung, “Learning the parts of objects by non-negative matrix factorization,” Nature, vol. 401, no. 6755, pp. 788–791, 1999.
[9] J. Eggert and E. Korner, “Sparse coding and NMF,” in Proc. IEEE Int. Conf. Neural Netw., 2004, pp. 2529–2533.
[10] P. Hoyer, “Non-negative matrix factorization with sparseness constraints,” J. Mach. Learn. Res., vol. 5, pp. 1457–1469, 2004.
[11] W. Dong, L. Zhang; G.Shi, and X. Wu, “Image deblurring and super-resolution by adaptive sparse domain selection and adaptive,” IEEE Trans Signal Process., vol. 20, no. 20, pp. 1838-1857, Jul. 2011.
[12] J. Yang, J. Wright, T. S. Huang, and Y. Ma, “Image Super-Resolution Via Sparse Representation,” IEEE Trans Signal Process. vol.19, no. 11, pp. 2861-2873, Nov. 2010.
[13] M. Elad and M. Aharon, “Image denoising via sparse and redundant representations over learned dictionaries,” IEEE Trans Signal Process. vol.15, no. 12, pp. 3736-3745, Dec. 2006.
[14] P. Chatterjee and P. Milanfar, “Patch-based near-optimal image denoising,” IEEE Trans Signal Process., vol. 21, no. 4, pp. 1635-1649, Apr. 2012.
[15] L. Vese, G. Sapiro, S. Osher, “Simultaneous structure and texture image inpainting,” IEEE Trans Signal Process., vol. 12, no. 8, pp. 882-889, Aug. 2003.
[16] M. Elad, J. L. Starck, P. Querre, and D. L. Donoho, “Simultaneous cartoon and texture image inpainting using morphological component analysis (MCA),” J. Appl. Comput. Harmon. Anal., vol. 19, pp. 340–358, Nov. 2005.
[17] J. K. Pillai, V. M. Patel, R. Chellappa, and N. K. Ratha, “Secure and robust iris recognition using random projections and sparse representations,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 9, pp. 1877-1893, Sep. 2011.
[18] L. W. Kang, C. Y. Hsu, H. W. Chen, C. S. Lu, C. Y. Lin, and S. C. Pei, “Feature-based sparse representation for image similarity assessment,” IEEE Trans. Multimedia, vol. 13, no. 5, pp. 1019-1030, Oct. 2011.
[19] 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. 2009.
[20] A. Mahalanobis and R. Muise, “Object specific image reconstruction using a compressive sensing architecture for application in surveillance systems,” IEEE Trans. Aerosp. Electron. Syst., vol. 45, no. 3, pp. 1167-1180, Jul. 2009.
[21] J. Wu, F. Liu, L. C. Jiao, X. Wang, and B. Hou, “Multivariate compressive sensing for image reconstruction in the wavelet domain: using scale mixture models,” IEEE Trans. Image Process., vol. 20, no. 12, pp. 3483-3493, Dec. 2011.
[22] C. Deng, W. Lin, B. Lee, and C. T. Lau, “Robust image coding based upon compressive sensing,” IEEE Trans. Multimedia, vol. 14, no. 2, pp. 278-290, Apr. 2012.
[23] J. Trzasko and A. Manduca, “Highly undersampled magnetic resonance image reconstruction via homotopic l0-minimization,” IEEE Trans. Med. Imag. vol. 28, no. 1, pp. 106-121, Jan. 2009.
[24] Q. F. Tan, P. G. Georgiou, and S. S. Narayanan, “Enhanced sparse imputation techniques for a robust speech recognition front-end,” IEEE Trans. Audio, Speech, Language Process., vol. 19, no. 8, pp. 2418-2429, Nov. 2011.
[25] J. Deller, J. H. L. Hansen, and J. G. Proakis, Discrete-Time Processing of Speech Signals. Piscataway, NJ: IEEE Press, 2000.
[26] S. F. Boll, “Suppression of acoustic noise in speech using spectral subtraction,” IEEE Trans. Acoust., Speech, Signal Process., vol. 27, no. 2, Apr. 1979.
[27] J. S. Lim and A. V. Oppenheim, “Enhancement and bandwidth compression of noisy speech,” Proc. IEEE, vol. 67, no. 12, pp. 1586-1604, Dec. 1979.
[28] M. Berouti, P. Schwarts, and J. Makhoul, “Enhancement of speech corrupted by acoustic noise,” Proc. Int. Conf. on Acoust., Speech and Signal Process., pp. 208-211, Apr. 1979.
[29] Y. Ephraim and D. Malah, “Speech enhancement usisng a minimum mean-square error log-spectral amplitude estimator,” IEEE Trans, Acoust., Speech and Signal Process., vol. 33, no. 2, pp. 443-445, Apr. 1985.
[30] Y. Ephraim and H. L. Trees, “A signal subspace approach for speech enhancement,” IEEE Trans. Acoust., Speech and Signal Process., vol. 3, pp. 251-266, Jul. 1995.
[31] D. P. W. Ellis and R. J. Weiss, “Model-based monaural source separation using a vector-qunatized phase-vocoder representation,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., 2006, vol. 5, pp. 957–960.
[32] S. Srinivasan, J. Samuelsson, and W. Kleijn, “Codebook driven shortterm predictor parameter estimation for speech enhancement,” IEEE Trans. Audio, Speech, Lang. Process., vol. 14, no. 1, pp. 163–176, Jan. 2006.
[33] S. Mallat and Z. Zhang, “Matching pursuits with time-frequency dictionaries,” IEEE Trans. Signal Process., vol. 41, no. 12, pp. 3397–3415, Dec. 1993.
[34] H. Rauhut, K. Schnass, and P. Vandergheynst, “Compressed sensing and redundant dictionaries,” IEEE Trans. Inf. Theory, vol. 54, no. 5, pp. 2210–2219, May 2008.
[35] D. Donoho and I. Johnstone, “Ideal spatial adaptation via wavelet shrinkage,” Biometrika, vol. 81, pp. 425–455, 1994.
[36] M. G. Jafari and M. D. Plumbley, “Fast dictionary learning for sparse representations of speech signals,” IEEE J. Sel. Topics Signal Process., vol. 5, no. 5, pp.1025-1031, Sep. 2011.
[37] J. Mairal, F. Bach, and J. Ponce, “Task-Driven Dictionary Learning,” IEEE trans. Pattern Anal. Mach. Intell., vol. 34, no. 4, pp. 791-804, Apr. 2012.
[38] S. Chen, D. L. Donoho, and M. A. Saunders, “Atomic decomposition by basis pursuit,” SIAM J. Sci Comp., vol. 20, no. 1, pp. 33–61, 1999.
[39] R. Tibshirani, “Regression shrinkage and selection via the LASSO,” Journal of the Royal Statistical Society (Series B), vol. 58, pp. 267–288, 1996.
[40] A. C. Gilbert and J. A. Tropp, “Signal recovery from random measurements via orthogonal matching pursuit,” IEEE Trans. Inf. Theory, vol. 53, no. 12, pp. 4655–4666, Dec. 2007.
[41] D. Needell and J. Tropp, “CoSaMP: Iterative signal recovery from incomplete and inaccurate samples,” Appl. Computat. Harmon. Anal., vol. 26, no. 3, pp. 301–321, May 2009.
[42] D. L. Donoho, Y. Tsaig, I. Drori, and J.-C. Starck, “Sparse solution of underdetermined linear equations by stagewise orthogonal matching pursuit,” Stanford Statistics Dept., Stanford Univ., Stanford, CA, TR-2006–2, Mar. 2006, Preprint.
[43] D. L. Donoho, Compressed Sensing, Manuscript, September 2004.
[44] E. Candès and T. Tao, “Decoding by linear programming,” IEEE Trans. Inform. Theory, vol. 51, no. 12, pp. 4203-4215, Dec. 2005.
[45] A. Varga, H. J. M. Steeneken, M. Tomlinson, and D. Jones, The Noisex-92 Study on the Effect of Additive Noise on Automatic Speech Recognition. Technical Report. Malvern, U.K.: DRA Speech Res. Unit, 1992.
|