Smooth signal extraction from instantaneous mixtures

Published versio

Evaluations on underdetermined blind source separation in adverse environments using time-frequency masking

The successful implementation of speech processing systems in the real world depends on its ability to handle adverse acoustic conditions with undesirable factors such as room reverberation and background noise. In this study, an extension to the established multiple sensors degenerate unmixing estimation technique (MENUET) algorithm for blind source separation is proposed based on the fuzzy c-means clustering to yield improvements in separation ability for underdetermined situations using a nonlinear microphone array. However, rather than test the blind source separation ability solely on reverberant conditions, this paper extends this to include a variety of simulated and real-world noisy environments. Results reported encouraging separation ability and improved perceptual quality of the separated sources for such adverse conditions. Not only does this establish this proposed methodology as a credible improvement to the system, but also implies further applicability in areas such as noise suppression in adverse acoustic environments

A fixed point solution for convolved audio source separation

We examine the problem of blind audio source separation using Independent Component Analysis (ICA). In order to separate audio sources recorded in a real recording environment, we need to model the mixing process as convolutional. Many methods have been introduced for separating convolved mixtures, the most successful of which require working in the frequency domain [1], [2], [3], [4]. This paper proposes a fixed-point algorithm for performing fast frequency domain ICA, as well as a method to increase the stability and enhance the performance of previous frequency domain ICA algorithms. 1

Nonlinear and Non-Gaussian Signal Processing - Simple mixture model for sparse overcomplete ICA

The use of mixture of Gaussians (MoGs) for noisy and overcomplete independent component analysis (ICA) when the source distributions are very sparse is explored. The sparsity model can often be justified if an appropriate transform, such as the modified discrete cosine transform, is used. Given the sparsity assumption, a number of simplifying approximations are introduced to the observation density that avoid the exponential growth of mixture components

An affine invariant function using PCA bases with an application to within-class object recognition

The problem of shape-based recognition of objects under affine transformations is considered. We focus on the construction of a robust and highly discriminative affine invariant function that can be used for within-class object recognition applications. Using the boundaries of the objects of interest, a training scheme, based on Principal Component Analysis (PCA), is proposed to derive a set of basis functions with desired properties. The derived bases are then used for the construction of a novel affine invariant function. The proposed invariant function is evaluated for the problem of aircraft silhouette identification and appears to achieve comparable performance to a popular wavelet-based affine invariant function. At the same time, the proposed framework is much simpler than that based on wavelet analysis. © 2007 IEEE

Robust recognition of planar shapes under affine transforms using principal component analysis

A scheme, based on principal component analysis (PCA), is proposed that can be used for the recognition of 2-D planar shapes under affine transformations. A PCA step is first used to map the object boundary to its canonical form, reducing the problem of the nonuniform sampling of the object contour introduced by the affine transformation. Then, a PCA-based scheme is employed to train a set of basis functions on the signals extracted from the objects' boundaries. The derived bases are used to analyze the boundary locally. Based on the theory of invariants and local boundary analysis, a novel invariant function is constructed. The performance of the proposed framework is compared with a standard wavelet-based approach with promising results. © 2007 IEEE

A unifying approach to moment-based shape orientation and symmetry classification

In this paper, the problem of moment-based shape orientation and symmetry classification is jointly considered. A generalization and modification of current state-of-the-art geometric moment-based functions is introduced. The properties of these functions are investigated thoroughly using Fourier series analysis and several observations and closed-form solutions are derived. We demonstrate the connection between the results presented in this work and symmetry detection principles suggested from previous complex moment-based formulations. The proposed analysis offers a unifying framework for shape orientation/symmetry detection. In the context of symmetry classification and matching, the second part of this work presents a frequency domain method, aiming at computing a robust moment-based feature set based on a true polar Fourier representation of image complex gradients and a novel periodicity detection scheme using subspace analysis. The proposed approach removes the requirement for accurate shape centroid estimation, which is the main limitation of moment-based methods, operating in the image spatial domain. The proposed framework demonstrated improved performance, compared to state-of-the-art methods. © 2008 IEEE

Fast wavelet-based pansharpening of multi-spectral images

Remote Sensing systems enhance the spatial quality of low-resolution Multi-Spectral (MS) images using information from Pan-chromatic (PAN) images under the pansharening framework. Most decimated multi-resolution pansharpening approaches upsample the low-resolution MS image to match the resolution of the PAN image. Consequently, a multi-level wavelet decomposition is performed, where the edge information from the PAN image is injected in the MS image. In this paper, the authors propose a pansharpening framework that eliminates the need of upsampling of the MS image, using a B-Spline biorthogonal wavelet decomposition scheme. The proposed method features similar performance t the state-of-the-art pansharpening methods without the extra computational cost induced by upsampling. © 2010 IEEE.</p