This book (Wiley-IEEE Press, Singapore, 2018) is a comprehensive guide to the theory and practice of signal enhancement and array signal processing. Written as a course textbook for senior undergraduate and graduate students. It introduces the fundamental principles, theory and applications of signal enhancement and array signal processing in an accessible manner. Lecture slides are available for this textbook, as well as Matlab codes for all the figures in the book. See also the Instructor Companion Site.

MATLAB software for speech enhancement based on optimally modified LSA (OM-LSA) speech estimator and improved minima controlled recursive averaging (IMCRA) noise estimation approach for robust speech enhancement can be downloaded HERE. The algorithms are described in: I. Cohen and B. Berdugo, Speech Enhancement for Non-Stationary Noise Environments, Signal Processing, Vol. 81, No. 11, Nov. 2001, pp. 2403-2418. I. Cohen, Noise Spectrum Estimation in Adverse Environments: Improved Minima Controlled Recursive Averaging, IEEE Trans. Speech and Audio Processing, Vol. 11, No. 5, Sep. 2003, pp. 466-475. I. Cohen and S. Gannot, Spectral Enhancement Methods, in Jacob Benesty, M. Mohan Sondhi and Yiteng (Arden) Huang (Eds.), Springer Handbook of Speech Processing, Springer, 2008, Part H, Ch. 44, pp. 873-901.

MATLAB software for transient interference suppression in speech signals based on the OM-LSA algorithm can be downloaded HERE. The algorithm is described in: A. Hirszhorn, D. Dov, R. Talmon and I. Cohen Transient Interference Suppression in Speech Signals Based on the OM-LSA Algorithm Proc. 13th International Workshop on Acoustic Echo and Noise Control, IWAENC-2012, Aachen, Germany, Sep. 4-6, 2012.

MATLAB implementation of the Short Time Fourier Transform (STFT) and Inverse Short Time Fourier Transform (ISTFT) can be downloaded HERE.

MATLAB software and data for Kernel-based Sensor Fusion with Application to Audio-Visual Voice Activity Detection can be downloaded HERE.
The algorithm is described in:

D. Dov, R. Talmon and I. Cohen
Kernel-based Sensor Fusion with Application to Audio-Visual Voice Activity Detection
IEEE Trans. Signal Processing, Vol. 64, Number 24, December 2016, pp. 6406-6416.

MATLAB implementation of Audio-Visual Voice Activity Detection Using Diffusion Maps can be downloaded HERE. The algorithm is described in: D. Dov, R. Talmon and I. Cohen Audio-Visual Voice Activity Detection Using Diffusion Maps IEEE Trans. Audio, Speech and Language Processing, Vol. 23, Number 4, April 2015, pp. 732-745.

MATLAB implementation of multiscale anomaly detection using diffusion maps can be downloaded HERE. The algorithm is described in: G. Mishne and I. Cohen Multiscale Anomaly Detection Using Diffusion Maps Special Issue of IEEE Journal of Selected Topics in Signal Processing on Anomalous Pattern Discovery for Spatial, Temporal, Networked, and High-Dimensional Signals, Vol. 7, Number 1, February 2013, pp. 111-123.

MATLAB implementation of linear system parametrization using diffusion kernels can be downloaded HERE. The algorithm is described in: R. Talmon, D. Kushnir, R. Coifman, I. Cohen and S. Gannot Parametrization of Linear Systems Using Diffusion Kernels IEEE Trans. Signal Processing, Vol. 60, Number 3, March 2012, pp. 1159-1173.

MATLAB software for Image Processing by Patch-Ordering can be downloaded HERE. The algorithm is described in: I. Ram, M. Elad and I. Cohen Image Processing using Smooth Ordering of its Patches IEEE Trans. Image Processing, Vol. 22, Number 7, July 2013, pp. 2764-2774.

MATLAB software for Image Denoising using NL-Means via Smooth Patch Ordering can be downloaded HERE. The algorithm is described in: I. Ram, M. Elad and I. Cohen Image Denoising Using NL-Means Via Smooth Patch Ordering Proc. 38th IEEE Internat. Conf. Acoust. Speech Signal Process., ICASSP-2013, Vancouver, Canada, May 26-31, 2013, pp. 1350-1354.