Master of Science in Electrical Engineering the University of Texas at Dallas

v ACKNOWLEDGEMENTS I would like to thank my advisor, Dr. Philipos C. Loizou, for his guidance in my studies and my work. He has offered me many helpful suggestions on conducting research and writing technical documents. defense committee and for their valuable feedback on this manuscript. I also thank Arunvijay Mani for helping me format the thesis. And finally I take this chance to thank all of my friends for being my driving force and standing by me through thick and thin. The quality and intelligibility of the speech in the presence of background noise can be improved by speech enhancement algorithms. This thesis addresses the issue of estimating the noise spectrum for speech enhancement applications. Two noise estimation algorithms are proposed for highly non-stationary noise environments. In method-1 a voice activity detector is first used to classify each frame of speech continuously into the speech present/absent frames, and the noise spectrum estimate is updated using a constant smoothing factor for speech absent frames and a frequency dependent smoothing factor for speech present frames. In method-2 the noise spectrum estimate is updated using a frequency dependent smoothing factor irrespective of speech present/absent frames. In both methods, the frequency dependent smoothing factor is calculated based on estimated speech presence probabilities in subbands. Speech presence is determined by computing the ratio of the noisy speech power spectrum to its local minimum, which is computed by averaging past values of the noisy speech power spectra with a look-ahead factor. The local minimum estimation algorithm adapts very quickly to highly non-stationary noise environments. This was confirmed with formal listening tests that indicated that the vii proposed noise estimation algorithms when integrated in speech enhancement were preferred over other noise estimation algorithms.