no code implementations • 27 Oct 2022 • Matan Karo, Arie Yeredor, Itshak Lapidot
The main focus of this paper is to suggest new representations for genuine and spoofed speech, based on the probability mass function (PMF) estimation of the audio waveforms' amplitude.
no code implementations • 21 Nov 2020 • Anatoly Khina, Arie Yeredor, Ram Zamir
It is common to assess the "memory strength" of a stationary process looking at how fast the normalized log-determinant of its covariance submatrices (i. e., entropy rate) decreases.
no code implementations • 28 Oct 2020 • Amir Weiss, Arie Yeredor
One-bit quantization has recently become an attractive option for data acquisition in cutting edge applications, due to the increasing demand for low power and higher sampling rates.
no code implementations • 28 Oct 2020 • Amir Weiss, Arie Yeredor
Blind calibration of sensors arrays (without using calibration signals) is an important, yet challenging problem in array processing.
no code implementations • 31 Aug 2020 • Amir Weiss, Arie Yeredor, Mark Shtaif
Orthogonal frequency division multiplexing (OFDM) has proven itself as an effective multi-carrier digital communication technique.
no code implementations • 30 Aug 2020 • Amir Weiss, Arie Yeredor, Sher Ali Cheema, Martin Haardt
In this paper we extend our results to the IVA problem, showing how the ML solution for the Gaussian model (with arbitrary covariance and cross-covariance matrices) takes the form of an extended SeDJoCo problem.
no code implementations • 30 Aug 2020 • Amir Weiss, Sher Ali Cheema, Martin Haardt, Arie Yeredor
As an immediate consequence of this result, we provide an asymptotically attainable lower bound on the resulting ISRs.
no code implementations • 30 Aug 2020 • Amir Weiss, Arie Yeredor
A novel blind estimate of the number of sources from noisy, linear mixtures is proposed.
no code implementations • 30 Aug 2020 • Amir Weiss, Arie Yeredor
However, we offer a substantial improvement over P-K's ordinary Least Squares (LS) estimates by using asymptotic approximations in order to obtain simple, non-iterative, (quasi-)linear Optimally-Weighted LS (OWLS) estimates of the sensors gains and phases offsets with asymptotically optimal weighting, based only on the empirical covariance matrix of the measurements.
no code implementations • 4 Jul 2017 • Ofir Lindenbaum, Moshe Salhov, Arie Yeredor, Amir Averbuch
We propose to set a scale parameter that is tailored to one of two types of tasks: classification and manifold learning.
no code implementations • 23 Aug 2015 • Ofir Lindenbaum, Arie Yeredor, Moshe Salhov, Amir Averbuch
The multi-view dimensionality reduction is achieved by defining a cross-view model in which an implied random walk process is restrained to hop between objects in the different views.