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This paper integrates our contributions in the domain of blind source separation (BSS) and blind source deconvolution (BSD) both in static and dynamic environments. We focus on the use of the state space formulation and the development of a generalized optimization framework, using Kullback-Liebler divergence as the performance measure subject to the… (More)

- G. Erten, F. M. Salam
- 1999

The paper presents a formulation and an implementation of a system for voice output extraction (VOX) in real-time and near-real-time realistic real-world applications. A key component includes voice-signal separation and recovery from a mixture in practical environments. The signal separation and extraction component includes several algorithmic modules… (More)

This paper presents two separate structures for the blind source recovery (BSR) of stochastically independent signal sources. We hypothesize linear state space models for both the mixing environment and the demixing (i.e. recovering) adaptive network. Separate algorithms for adaptive estimation of parameters for the feedforward and feedback recovering… (More)

We present a novel performance index to measure the statistical independence of data sequences and apply it to the framework of blind source recovery (BSR) that includes blind source separation, deconvolution and equalization. This performance index is capable of measuring the mutual independence of data sequences directly from the data. This information… (More)

This paper addresses the issue of automatic word/sentence boundary detection in both quiet and noisy environments. We propose to use an entropy based contrast function between the speech segments and the background noise. A simplified data based scheme of computing the entropy of the speech data is presented. The entropy-based contrast exhibits… (More)

- Khurram Waheed, Fathi M. Salam
- ISCAS
- 2002

The paper discusses State Space Blind Source Recovery (BSR) for minimum phase and non-minimum phase mixtures of gaussian and non-gaussian distributions. The State Space Natural Gradient approach results in compact iterative update laws for BSR. Two separate state space algorithms for minimum phase and non-minimum phase mixing environments are presented. The… (More)

Abstract This paper discusses the implementation of our proposed algorithms for Blind source Recovery based on constrained optimization using the state-space framework. Two simulation examples are presented where the mixing environment is modeled as FIR and IIR, respectively. The rate of convergence using the proposed implementation for these particular… (More)

Blind Source Recovery (BSR) is an interesting autonomous and unsupervised stochastic adaptation problem that includes the well-known blind adaptive problems of Blind Source Separation (BSS), Deconvolution (BSD) and Equalization (BSE). BSR includes also the nonlinear case and hences focuses on reproducing or estimating the source signals even if environment… (More)

The paper describes the use of the state space and the natural gradient for the demixing of sources mixed in a non-minimum phase convolutive environment. Non-minimum phase implies that some or all of the zeros of the mixing environment lie outside the unit circle, and as such the theoretical inverse or the requisite demixing system becomes unstable due to… (More)

We report the successful use of continuous wavelet transforms applied to atomic force microscope data sets for landmark recognition of biological features. The data sets were images of mixed red and white blood cells. Contrast enhancement followed by continuous wavelet transform of the data was used to successfully distinguish erythrocytes from neutrophil… (More)