Forensic Speaker Verification in Noisy Environmental by Enhancing the Speech Signal Using ICA Approach
 M. I. Mandasari, M. McLaren, and D. A. van Leeuwen, ”The effect of
noise on modern automatic speaker recognition systems,” in IEEE Int.
Conf. Acoust., Speech Signal Process., 2012, pp. 4249-4252.
 G. S. Morrison, P. Rose, and C. Zhang, ”Protocol for the collection
of databases of recordings for forensic-voice-comparison research and
practice,”Australian J. Forensic Sci., vol. 44, pp. 155-167, 2012.
 J. P. Campbell, W. Shen, W. M. Campbell, R. Schwartz, J. F. Bonastre,
and D. Matrouf, ”Forensic speaker recognition,” IEEE Signal Process.
Mag., pp. 95-103, 2009.
 Berouti , M., Schwartz, R. and Makhoul, J., “Enhancement of speech
corrupted by acoustic noise”, IEEE Int. Conf. Acoust., Speech, Signal
Process., vol. 4, 1979, pp. 208-211.
 Donho, D.L and Johnston, I.M., “Ideal spatial adapation by wavelet
shrinkage”, Biometrika J., vol. 81, pp. 425-455,1994.
 A. K. H. AL-ALI, D. Dean, B. Senadji, and V. Chandran,”Comparison of
speech enhancement algorithms for forensic applications,”in 16th Speech
science and technology conference, Sydney, 2016.
 H. Liang, J. Rosca, and R. Balan, ”Independent component analysis
based single channel speech enhancement,” in 3rd IEEE Int. Symp. Signal
Process. Inform. Technology, 2003, pp. 522-525.
 H. Li, H. Wang, and B. Xiao, ”Blind separation of noisy mixed
speech signals based on wavelet transform and Independent Component
Analysis,” in 8th Int. Conf. Signal Process., 2006.
 Hyvarinen, A. and Oja, E., “Independent component analysis: algorithms
and applications”, Neural Netw., vol. 13, no. 4, pp. 411-430, 2000.
 H.-y. Li, Q.-h. Zhao, G.-l. Ren, and B.-j. Xiao, ”Speech Enhancement
Algorithm Based on Independent Component Analysis,” in 5th Int. Conf.
Natural Computation, 2009, pp. 598-602.
 D. B. Dean, S. Sridharan, R. J. Vogt, and M. W. Mason, ”The
QUT-NOISE-TIMIT corpus for the evaluation of voice activity detection
algorithms,” in Proc. Interspeech, Makuhari, Japan, 2010, pp. 26-30.
 R. S. Holambe and M. S. Deshpande, ”Noise Robust Speaker
Identification: Using Nonlinear Modeling Techniques,” in Forensic
Speaker Recognition, Ed: Springer, 2012, pp. 153-182.
 A. Varga and H. J. M. Steeneken,”Assessment for automatic speech
recognition:II. NOISEX-92: A database and an experiment to study the
effect of additive noise on speech recognition systems,” Speech Commun.,
vol. 12, no. 3, pp. 247-251, 1993.
 S. O. Sadjadi, M. Slaney, and L. Heck, ”MSR identity toolbox
- A matlab toolbox for speaker recognition research”, Microsoft
Research,Conversational Systems Research Center (CSRC), 2013.
 G. S. Morrison, C. Zhang, E. Enzinger, F. Ochoa, D. Bleach, M. Johnson,
B. K. Folky, S. Desouza, N. Cumminus, D. Chow. (2015). Forensic
database of voice recordings of 500+ Australian English speakers.
 J. Sohn, N. S. Kim, and W. Sung, ”A statistical model-based voice
activity detection,” IEEE Signal Pocess. Lett., vol. 6, no.1, pp. 1-3, Jan.