Monaural sound source separation using covariance profile of partials

This paper addresses the problem of separation of pitched sounds in monaural recordings. We present a novel feature for the estimation of parameters of overlapping harmonics which considers the covariance of partials of pitched sounds. Sound templates are formed from the monophonic parts of the mixture recording. A match for every note is found among these templates on the basis of covariance profile of their harmonics. The matching template for the note provides the second order characteristics for the overlapped harmonics of the note. The algorithm is tested on the RWC music database instrument sounds. The results clearly show that the covariance characteristics can be used to reconstruct overlapping harmonics effectively.

An extension of slow feature analysis for nonlinear blind source separation

We present and test an extension of slow feature analysis as a novel approach to nonlinear blind source separation. The algorithm relies on temporal correlations and iteratively reconstructs a set of statistically independent sources from arbitrary nonlinear instantaneous mixtures. Simulations show that it is able to invert a complicated nonlinear mixture of two audio signals with a high reliability. The algorithm is based on a mathematical analysis of slow feature analysis for the case of input data that are generated from statistically independent sources. © 2014 Henning Sprekeler, Tiziano Zito and Laurenz Wiskott.

MISEP method for postnonlinear blind source separation

In this letter, a standard postnonlinear blind source separation algorithm is proposed, based on the MISEP method, which is widely used in linear and nonlinear independent component analysis. To best suit a wide class of postnonlinear mixtures, we adapt the MISEP method to incorporate a priori information of the mixtures. In particular, a group of three-layered perceptrons and a linear network are used as the unmixing system to separate sources in the postnonlinear mixtures, and another group of three-layered perceptron is used as the auxiliary network. The learning algorithm for the unmixing system is then obtained by maximizing the output entropy of the auxiliary network. The proposed method is applied to postnonlinear blind source separation of both simulation signals and real speech signals, and the experimental results demonstrate its effectiveness and efficiency in comparison with existing methods.

On the performance of a blind source separation based I/Q-corrector

I and Q Channel phase and gain mismatches are of great concern in communications receiver design. In this paper we carry out a detailed performance analysis of the Blind-Source Seperation (BSS) based imbalance compensation structure. The results indicate that the BSS structure can offer adequate performance for most communication systems. Since the compensation is carried out before any modulation specific processing, the proposed compensation method works with all standard modulation formats.

On the structure and performance of a novel blind source separation based carrier phase synchronization error compensator

In this paper we carry out a detailed performance analysis of a novel blind-source-seperation (BSS) based DSP algorithm that tackles the carrier phase synchronization error problem. The results indicate that the mismatch can be effectively compensated during the normal operation as well as in the rapidly changing environments. Since the compensation is carried out before any modulation specific processing, the proposed method works with all standard modulation formats and lends itself to efficient real-time custom integrated hardware or software implementations.

Blind source separation based on phase and frequency redundancy of cyclostationary signals

This paper presents a new method for blind source separation by exploiting phase and frequency redundancy of cyclostationary signals in a complementary way. It requires a weaker separation condition than those methods which only exploit the phase diversity or the frequency diversity of the source signals. The separation criterion is to diagonalize a polynomial matrix whose coefficient matrices consist of the correlation and cyclic correlation matrices, at time delay .TAU. = 0, of multiple measurements. An algorithm is proposed to perform the blind source separation. Computer simulation results illustrate the performance of the new algorithm in comparison with the existing ones.

Adaptive blind source separation using constant modulus criterion and signal mutual information

We address the problem of adaptive blind source separation (BSS) from instantaneous multi-input multi-output (MIMO) channels. It is known that the constant modulus (CM) criterion can be used to extract unknown source signals. However, the existing CM based algorithms normally extract the source signals in a serial manner. Consequently, the accuracy in extracting each source signal, except for the first one, depends on the accuracy of previous source extraction. This estimation error propagation (accumulation) causes severe performance degradation. In this paper, we propose a new adaptive separation algorithm that can separate all source signals simultaneously by directly updating the separation matrix. The superior performance of the new algorithm is demonstrated by simulation examples

Blind source separation using second-order cyclostationary statistics

This paper studies the blind source separation (BSS) problem with the assumption that the source signals are cyclostationary. Identifiability and separability criteria based on second-order cyclostationary statistics (SOCS) alone are derived. The identifiability condition is used to define an appropriate contrast function. An iterative algorithm (ATH2) is derived to minimize this contrast function. This algorithm separates the sources even when they do not have distinct cycle frequencies .

Underdetermined blind source separation based on relaxed sparsity condition of sources

Recently, Aissa-El-Bey et al. have proposed two subspacebased methods for underdetermined blind source separation (UBSS) in time-frequency (TF) domain. These methods allow multiple active sources at TF points so long as the number of active sources at any TF point is strictly less than the number of sensors, and the column vectors of the mixing matrix are pairwise linearly independent. In this correspondence, we first show that the subspace-based methods must also satisfy the condition that any M × M submatrix of the mixing matrix is of full rank. Then we present a new UBSS approach which only requires that the number of active sources at any TF point does not exceed that of sensors. An algorithm is proposed to perform the UBSS.