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Found 11 papers, 0 papers with code
Time-Domain Based Embeddings for Spoofed Audio Representation
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.
Monotonicity of the Trace-Inverse of Covariance Submatrices and Two-Sided Prediction
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.
Enhanced Blind Calibration of Uniform Linear Arrays with One-Bit Quantization by Kullback-Leibler Divergence Covariance Fitting
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.
Non-Iterative Blind Calibration of Nested Arrays with Asymptotically Optimal Weighting
Blind calibration of sensors arrays (without using calibration signals) is an important, yet challenging problem in array processing.
Iterative Symbol Recovery For Power Efficient DC Biased Optical OFDM Systems
Orthogonal frequency division multiplexing (OFDM) has proven itself as an effective multi-carrier digital communication technique.
The Extended "Sequentially Drilled" Joint Congruence Transformation and its Application in Gaussian Independent Vector Analysis
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.
Performance Analysis of the Gaussian Quasi-Maximum Likelihood Approach for Independent Vector Analysis
As an immediate consequence of this result, we provide an asymptotically attainable lower bound on the resulting ISRs.
Blind Determination of the Number of Sources Using Distance Correlation
A novel blind estimate of the number of sources from noisy, linear mixtures is proposed.
Asymptotically Optimal Blind Calibration of Uniform Linear Sensor Arrays for Narrowband Gaussian Signals
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.
Kernel Scaling for Manifold Learning and Classification
We propose to set a scale parameter that is tailored to one of two types of tasks: classification and manifold learning.
MultiView Diffusion Maps
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.
