We discuss overcomplete blind source separation (BSS), that is separation with more sources than sensors. Finding the mixing matrix solves the linear quadratic BSS problem; in overcomplete BSS however, it is then still not clear and not even unique how to get the sources from the mixtures and the mixing matrix. We therefore follow Bofill and Zibulevsky in [5] and many others and take a two-step...

Overcomplete blind source separation (BSS) tries to recover more sources from less sensor signals. We present a new approach based on an estimated histogram of the sensor data; we search for the points fulfilling the overcomplete Geometric Convergence Condition, which has been shown to be a limit condition of overcomplete geometric BSS [1]. The paper concludes with an example and a comparison o...

A first step to perform a competition of methods for source separation is the development of a testsuite that supports the development and evaluation of blind source separation (BSS) algorithms in a highly automated way. The concept of our testsuite is presented and it is shown how the testsuite can be used to apply BSS-algorithms to four standard sub-problems. To compare the performance of arb...

The field of Signal Processing has witnessed the strong emergence of a new technique, the Blind Signal Processing (BSP) which is based on sound theoretical foundation. An offshoot of the BSP is known as Blind Source Separation (BSS). This digital signal processing techniques have a wide and varied potential applications. The term blind is indicative of the fact that both the source signal and t...

Successful speech enhancement by convolutive blind source separation (BSS) techniques requires careful design of all aspects of the chosen separation method. The conventional strategy for system initialization in both timeand frequency-domain BSS involves a diagonal center-spike FIR filter matrix and no data preprocessing; however, this strategy may not be the best for any chosen separation alg...

A new approach for convolutive blind source separation (BSS) using penalty functions is proposed in this paper. Motivated by nonlinear programming techniques for the constrained optimization problem, it converts the convolutive BSS into a joint diagonalization problem with unconstrained optimization. Theoretical analyses together with numerical evaluations reveal that the proposed method not on...

Blind Source Separation (BSS) arises in a variety of fields in speech processing such as speech enhancement, speakers diarization and identification. Generally, methods for BSS consider several observations of the same recording. Single microphone analysis is the worst underdetermined case, but, it is also the more realistic one. In this article, the autoregressive structure (short term predict...

Blind source separation based on time-frequency distributions (TFD’s) allows the separation of Gaussian sources with identical spectral shape but different t-f localization properties. As TFD’s spread noise power while localizing the source energy in the t-f domain, the time-frequency BSS is characterized by robustness of separation and improved overall performance. However, quadratic TFD’s, wh...

Independent component analysis (ICA) as well as blind source separation (BSS) often faces the problem of assigning the independent or uncorrelated components estimated with ICA or BSS techniques to underlying source signals, artifacts or noise contributions. In this work an automatic assignment tool is presented which uses a priori knowledge about the form of some of the signals to be extracted...

This paper deals with a process of denoising based on a Blind Source Separation (BSS) method. This technique is inserted in an experimental device of nondestructive testing. Its excitation is a laser beam and its detectors are pyroelectric sensors. The latter are sensitive to the temperature. As they are also piezoelectric, they are particularly sensitive to the environmental noise. Therefore, ...