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In this paper, a novel method based on the entropy estimation of the observation space eigenvalues is proposed to estimate the number of independent sources impinging on a sensor array. In this method we do not need to know a priori information about the noise model and we can use it in any Gaussian or non-Gaussian model of observations and noise. Our analytical results show that the proposed algorithm is consistent and an approximation for probability of false alarm and an upper bound for probability of missed detection are derived analytically. The performance of the proposed algorithm is compared with the existing methods in the presence of Gaussian and non-Gaussian noise via the simulations. It is shown that this information theoretic method called EEE, has a better performance than those methods in the literature, especially in non-Gaussian noise environment. [ABSTRACT FROM AUTHOR]

Determining the frequencies of multiple resolvable exponentials is an important problem due to its application in diverse areas in science and engineering. In this paper, frequency estimation of two-dimensional (2-D) sinusoids is addressed. With the use of the periodogram in frequency domain, the required harmonics are first located coarsely. The characteristics of the 2-D spectrum is then analyzed, and the accurate estimates of the parameters are retrieved using an interpolation method iteratively. It is proved that at sufficiently high signal-to-noise ratio conditions, the harmonic estimates are asymptotically unbiased, and their variances are also analyzed. Furthermore, when only part of the data is observed, the proposed algorithm is tailored to get fast and accurate estimation results. Computer simulations are also included to compare the proposed approach with conventional 2-D harmonic retrieval schemes in terms of root mean square error performance and computational complexity. [ABSTRACT FROM AUTHOR]

Abstract: Blind source separation (BSS) has attained much attention in signal processing society due to its ‘blind’ property and wide applications. However, there are still some open problems, such as underdetermined BSS, noise BSS. In this paper, we propose a Bayesian approach to improve the separation performance of instantaneous mixtures with non-stationary sources by taking into account the internal organization of the non-stationary sources. Gaussian mixture model (GMM) is used to model the distribution of source signals and the continuous density hidden Markov model (CDHMM) is derived to track the non-stationarity inside the source signals. Source signals can switch between several states such that the separation performance can be significantly improved. An expectation-maximization (EM) algorithm is derived to estimate the mixing coefficients, the CDHMM parameters and the noise covariance. The source signals are recovered via maximum a posteriori (MAP) approach. To ensure the convergence of the proposed algorithm, the proper prior densities, conjugate prior densities, are assigned to estimation coefficients for incorporating the prior information. The initialization scheme for the estimates is also discussed. Systematic simulations are used to illustrate the performance of the proposed algorithm. Simulation results show that the proposed algorithm has more robust separation performance in terms of similarity score in noise environments in comparison with the classical BSS algorithms in determined mixture case. Additionally, since the mixing matrix and the sources are estimated jointly, the proposed EM algorithm also works well in underdetermined case. Furthermore, the proposed algorithm converges quickly with proper initialization. [Copyright &y& Elsevier]

Abstract: This paper presents the development and implementation of a variable rate time-scaling expansion system for speech signals, based on the pitch information, in which only the voiced segments are expanded, keeping the unvoiced and silence segments unchanged. The proposed system was first evaluated by computer simulation and then implemented on a digital signal processor (DSP). Time-domain, frequency-domain, mean opinion score (MOS) and diagnostic rhyme test (DRT) evaluations were done to test the actual performance of developed algorithm, which show that the proposed system allows improving the learning level of foreign language students as well as the understanding ability of elderly people. Objective tests also were carried out in order to probe similarity between the original and the expanded signals. Applying an iterative refinement of the C source code it was possible to obtain a real-time implementation. The current implemented algorithm requires 11 kwords program memory and about 9 million of floating point operations per second (MFLOPS). [Copyright &y& Elsevier]