David Malah
Technion – Israel Institute of Technology
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Featured researches published by David Malah.
IEEE Transactions on Acoustics, Speech, and Signal Processing | 1985
Yariv Ephraim; David Malah
In this correspondence we derive a short-time spectral amplitude (STSA) estimator for speech signals which minimizes the mean-square error of the log-spectra (i.e., the original STSA and its estimator) and examine it in enhancing noisy speech. This estimator is also compared with the corresponding minimum mean-square error STSA estimator derived previously. It was found that the new estimator is very effective in enhancing the noisy speech, and it significantly improves its quality.
IEEE Transactions on Signal Processing | 2007
Oleg Kuybeda; David Malah; Meir Barzohar
In this paper, we address the problem of redundancy-reduction of high-dimensional noisy signals that may contain anomaly (rare) vectors, which we wish to preserve. For example, when applying redundancy reduction techniques to hyperspectral images, it is essential to preserve anomaly pixels for target detection purposes. Since rare-vectors contribute weakly to the -norm of the signal as compared to the noise, -based criteria are unsatisfactory for obtaining a good representation of these vectors. The proposed approach combines and norms for both signal-subspace and rank determination and considers two aspects: One aspect deals with signal-subspace estimation aiming to minimize the maximum of data-residual -norms, denoted as , for a given rank conjecture. The other determines whether the rank conjecture is valid for the obtained signal-subspace by applying Extreme Value Theory results to model the distribution of the noise -norm. These two operations are performed alternately using a suboptimal greedy algorithm, which makes the proposed approach practically plausible. The algorithm was applied on both synthetically simulated data and on a real hyperspectral image producing better results than common -based methods.
CVGIP: Graphical Models and Image Processing | 1991
R. Fabian; David Malah
Abstract Most image deblurring methods assume knowledge of the point spread function (PSF) causing the blur. In this work we address the problem of identifying the characterizing parameter of the PSF, which corresponds to motion or out-of-focus blur, from blurred and noisy images. The observation that the spectra of these blurring functions have periodic (or almost periodic) zeros is the basis of an already known blur identification method in the cepstral domain. However, this method is found to be highly sensitive to noise. In this paper we propose the following improvements on the above method: First, adding a preprocessing stage for noise reduction, using a modified spectral subtraction approach—with a median-complement filter to estimate the noise. Second, applying an adaptive, quefrency-varying, comb-like window (lifter) in the cepstral domain to enhance the blur parameter identification. The robustness of the proposed algorithm is demonstrated by its ability to identify the blur function parameters from noisy blurred images with signal-to-noise ratio down to 0 dB for motion blur and 3 dB for out-of-focus blur, as compared to 20 dB for the Original method.
Signal Processing | 1997
Israel Cohen; Shalom Raz; David Malah
Abstract In this work, a shifted wavelet packet (SWP) library, containing all the time shifted wavelet packet bases, is defined. A corresponding shift-invariant wavelet packet decomposition (SIWPD) search algorithm for a ‘best basis’ is introduced. The search algorithm is representable by a binary tree, in which a node symbolizes an appropriate subspace of the original signal. We prove that the resultant ‘best basis’ is orthonormal and the associated expansion, characterized by the lowest information cost, is shift-invariant. The shift invariance stems from an additional degree of freedom, generated at the decomposition stage and incorporated into the search algorithm. The added dimension is a relative shift between a given parent node and its respective children nodes. We prove that for any subspace it suffices to consider one of two alternative decompositions, made feasible by the SWP library. These decompositions correspond to a zero shift and a 2−l relative shift where l denotes the resolution level. The optimal relative shifts, which minimize the information cost, are estimated using finite depth subtrees. By adjusting their depth, the quadratic computational complexity associated with SIWPD may be controlled at the expense of the attained information cost down to O (Nlog2N).
IEEE Transactions on Image Processing | 1998
Renato Kresch; David Malah
This paper presents new properties of the discrete morphological skeleton representation of binary images, along with a novel coding scheme for lossless binary image compression that is based on these properties. Following a short review of the theoretical background, two sets of new properties of the discrete morphological skeleton representation of binary images are proved. The first one leads to the conclusion that only the radii of skeleton points belonging to a subset of the ultimate erosions are needed for perfect reconstruction. This corresponds to a lossless sampling of the quench function. The second set of new properties is related to deterministic prediction of skeletonal information in a progressive transmission scheme. Based on the new properties, a novel coding scheme for binary images is presented. The proposed scheme is suitable for progressive transmission and fast implementation. Computer simulations, also presented, show that the proposed coding scheme substantially improves the results obtained by previous skeleton-based coders, and performs better than classical coders, including run-length/Huffman, quadtree, and chain coders. For facsimile images, its performance can be placed between the modified read (MR) method (K=4) and modified modified read (MMR) method.
international conference on acoustics, speech, and signal processing | 1993
Dan Chazan; Yoram Stettiner; David Malah
The problem of optimally estimating (in the maximum-likelihood sense) the pitch of each of several speakers talking simultaneously is addressed. This information is needed in systems which perform co-channel speech separation. A multipitch model is proposed which is used in conjunction with an EM (expectation maximization)-based iterative estimation scheme. The pitch period of each speaker is allowed to vary linearly in the analysis interval, thus offering improved cochannel speech separation. The proposed algorithm is shown to outperform standard pitch detection algorithms in detecting the pitch of simultaneous speakers. The proposed multipitch detection algorithm has potential in improving the performance of speaker separation and in interference suppression systems.<<ETX>>
IEEE Transactions on Pattern Analysis and Machine Intelligence | 2004
Amir Helzer; Meir Barzohar; David Malah
This work deals with fitting 2D and 3D implicit polynomials (IPs) to 2D curves and 3D surfaces, respectively. The zero-set of the polynomial is determined by the IP coefficients and describes the data. The polynomial fitting algorithms proposed in this paper aim at reducing the sensitivity of the polynomial to coefficient errors. Errors in coefficient values may be the result of numerical calculations, when solving the fitting problem or due to coefficient quantization. It is demonstrated that the effect of reducing this sensitivity also improves the fitting tightness and stability of the proposed two algorithms in fitting noisy data, as compared to existing algorithms like the well-known 3L and gradient-one algorithms. The development of the proposed algorithms is based on an analysis of the sensitivity of the zero-set to small coefficient changes and on minimizing a bound on the maximal error for one algorithm and minimizing the error variance for the second. Simulation results show that the proposed algorithms provide a significant reduction in fitting errors, particularly when fitting noisy data of complex shapes with high order polynomials, as compared to the performance obtained by the above mentioned existing algorithms.
convention of electrical and electronics engineers in israel | 1995
H. Krupnik; David Malah; E. Karnin
Fractal representation of images is based on mappings between similar regions within an image (also known as IFS). Such a representation can be applied to image coding and to increase image resolution. One of the main drawbacks of conventional fractal representation is the fact that the mappings are between blocks. As a result, the reconstructed image may suffer from disturbing blockiness. In this work we present a method for mapping similar regions within an image in the wavelet domain. We first show how to use the Haar wavelet transform coefficients to find mappings which are identical to conventional blockwise mappings. The union of these mappings, between sets of wavelet coefficients, can be interpreted as a prediction of higher bands of a signal from its lower band. Changing the mother-wavelet to other than Haar, creates mappings which are between regions which smoothly decay towards their borders, thus reducing the blockiness, as well as improving the PSNR of the reconstructed image.
IEEE Transactions on Audio, Speech, and Language Processing | 2011
Stas Tiomkin; David Malah; Slava Shechtman; Zvi Kons
Concatenative synthesis and statistical synthesis are the two main approaches to text-to-speech (TTS) synthesis. Concatenative TTS (CTTS) stores natural speech features segments, selected from a recorded speech database. Consequently, CTTS systems enable speech synthesis with natural quality. However, as the footprint of the stored data is reduced, desired segments are not always available in the stored data, and audible discontinuities may result. On the other hand, statistical TTS (STTS) systems, in spite of having a smaller footprint than CTTS, synthesize speech that is free of such discontinuities. Yet, in general, STTS produces lower quality speech than CTTS, in terms of naturalness, as it is often sounding muffled. The muffling effect is due to over-smoothing of model-generated speech features. In order to gain from the advantages of each of the two approaches, we propose in this work to combine CTTS and STTS into a hybrid TTS (HTTS) system. Each utterance representation in HTTS is constructed from natural segments and model generated segments in an interweaved fashion via a hybrid dynamic path algorithm. Reported listening tests demonstrate the validity of the proposed approach.
international conference on image processing | 2007
Boaz Ophir; David Malah
Show-Through is a common occurrence when scanning duplex printed documents. The back-side printing shows through the paper, contaminating the front side image. Previous work modeled the problem as a non-linear convolutive mixture of images and offered solutions based on decorrelation. In this work we propose a cleaning process based on a Blind Source Separation approach. We define a cost function incorporating the non-linear mixing model in a mean-squared error term, along with a regularization term based on Total-Variation. We propose a location dependent regularization tradeoff, preserving image edges while removing show-through edges. The images and mixing parameters are estimated using an alternating minimization process, with each stage using only convex optimization methods. The resulting images exhibit significantly lower show-through, both visibly and in objective measures.