A modified underdetermined blind source separation algorithm using competitive learning

The problem of underdetermined blind source separation is addressed. An advanced classification method based upon competitive learning is proposed for automatically determining the number of active sources over the observation. Its introduction in underdetermined blind source separation successfully overcomes the drawback of an existing method, in which the goal of separating more sources than the number of available mixtures is achieved by exploiting the sparsity of the non-stationary sources in the time-frequency domain. Simulation studies are presented to support the proposed approach

Bounds for the mixing parameter within the CC-CMA algorithm applied in non ideal multiuser environments

We derive new bounds for the mixing parameter, γ, within the cross-correlation constant modulus algorithm (CC-CMA) for blind source separation and equalization in non-ideal multiuser environments. Channel undermodelling and noise are considered when the complex sources are circularly symmetric. These tighter bounds are obtained by surface topography of the error performance surface of the CC-CMA algorithm, and replace earlier work which suggested that γ>4/3. The validity of the bounds is confirmed by simulation studie

A new cross-correlation and constant modulus type algorithm for PAM-PSK signals

We address the problem of blind recovery of multiple sources from their linear convolutive mixture with the cross-correlation and constant modulus algorithm. The steady state mean-squared error of this algorithm is first derived to justify the proposal of a new cross-correlation and constant modulus type algorithm for PAM-PSK type non-constant modulus signals. Simulation studies are presented to support the improved steady-state performance of the new algorithm

Non-negative matrix factorization for note onset detection of audio signals

A novel approach using non-negative matrix factorization (NMF) for onset detection of musical notes from audio signals is presented. Unlike most commonly used conventional approaches, the proposed method exploits a new detection function constructed from the linear temporal bases that are obtained from a non-negative matrix decomposition of musical spectra. Both first-order difference and psychoacoustically motivated relative difference functions of the temporal profile are considered. As the approach works directly on input data, no prior knowledge or statistical information is thereby required. A practical issue of the choice of the factorization rank is also examined experimentally. Numerical examples are provided to show the performance of the proposed method

Exploitation of source nonstationarity in underdetermined blind source separation with advanced clustering techniques

The problem of blind source separation (BSS) is investigated. Following the assumption that the time-frequency (TF) distributions of the input sources do not overlap, quadratic TF representation is used to exploit the sparsity of the statistically nonstationary sources. However, separation performance is shown to be limited by the selection of a certain threshold in classifying the eigenvectors of the TF matrices drawn from the observation mixtures. Two methods are, therefore, proposed based on recently introduced advanced clustering techniques, namely Gap statistics and self-splitting competitive learning (SSCL), to mitigate the problem of eigenvector classification. The novel integration of these two approaches successfully overcomes the problem of artificial sources induced by insufficient knowledge of the threshold and enables automatic determination of the number of active sources over the observation. The separation performance is thereby greatly improved. Practical consequences of violating the TF orthogonality assumption in the current approach are also studied, which motivates the proposal of a new solution robust to violation of orthogonality. In this new method, the TF plane is partitioned into appropriate blocks and source separation is thereby carried out in a block-by-block manner. Numerical experiments with linear chirp signals and Gaussian minimum shift keying (GMSK) signals are included which support the improved performance of the proposed approaches

Non-Negative Matrix Factorization for Note Onset Detection of Audio Signals

A novel approach using non-negative matrix factorization (NMF) for onset detection of musical notes from audio signals is presented. Unlike most commonly used conventional approaches, the proposed method exploits a new detection function constructed from the linear temporal bases that are obtained from a non-negative matrix decomposition of musical spectra. Both first-order difference and psychoacoustically motivated relative difference functions of the temporal profile are considered. As the approach works directly on input data, no prior knowledge or statistical information is thereby required. A practical issue of the choice of the factorization rank is also examined experimentally. Numerical examples are provided to show the performance of the proposed method

Fast convergence algorithms for joint blind equalization and source separation based upon the cross-correlation and constant modulus criterion

To solve the problem of joint blind equalization and source separation, two new quasi-Newton adaptive algorithms with rapid convergence property are proposed, based on the cross-correlation and constant modulus (CC-CM) criterion, namely the block-Shanno cross-correlation and constant modulus algorithm (BS-CCCMA) and the fast quasi-Newton crosscorrelation and constant modulus algorithm (FQN-CCCMA). Simulations studies are used to show that the convergence properties of these algorithms are much improved upon those of the conventional LMS-CCCMA algorith

From 2D layer to 2D → 3D parallel interpenetration: Syntheses, structures and luminescent properties

<div><p></p><p>Two new coordination polymers based on 1,1’-(1,4-butanediyl)bis(imidazole) (bbi) and 5-hydroxyisophthalic acid (OH-BDC), [Co₂(HO-BDC)₂(bbi)₂]·H₂O (<b>1</b>) and [Zn(HO-BDC)(bbi)] (<b>2</b>), have been hydrothermally synthesized. The complexes were characterized by single crystal X-ray diffraction, IR and elemental analysis. The structure determination reveals that <b>1</b> manifests a deeply corrugated 2D layer with a (4,4) lattice. For <b>2</b>, there is a highly undulating 2D (4,4) layer structure. The layers penetrate by each other to give a 2D → 3D parallel interpenetrating network. Fluorescence properties and thermal stabilities of <b>1</b> and <b>2</b> in the solid state have been studied.</p></div

From 2<b>-</b>D layer to 2<b>-</b>D → 3<b>-</b>D parallel interpenetration: syntheses, structures and luminescent properties

<p>Two new coordination polymers based on 1,1′-(1,4-butanediyl)bis(imidazole) (bbi) and 5-hydroxyisophthalic acid (OH-BDC), [Co₂(HO-BDC)₂(bbi)₂]·H₂O (<b>1</b>) and [Zn(HO-BDC)(bbi)] (<b>2</b>), have been hydrothermally synthesized. The complexes were characterized by single-crystal X-ray diffraction, IR, and elemental analysis. The structure determination reveals that <b>1</b> manifests a deeply corrugated 2-D layer with a (4,4) lattice. For <b>2</b>, there is a highly undulating 2-D (4,4) layer structure. The layers penetrate by each other to give a 2-D → 3-D parallel interpenetrating network. Fluorescence properties and thermal stabilities of <b>1</b> and <b>2</b> in the solid state have been studied.</p