A Fast and Efficient Frequency-Domain Method for Convolutive Blind Source Separation

In this paper, the problem of blind separation of a convolutive mixture of audio signals is considered. A fast and efficient frequency-domain blind source separation (BSS) method using Independent component analysis (ICA) is investigated. The main difficulties of this approach lie in the so called permutation and amplitude problems. In order to solve the permutation ambiguity, the final value of the ICA derived separation matrix of one frequency bin, is used to initialize the ICA iterations in the next frequency bin. The amplitude problem is addressed by utilizing the elements in the inverse of the separation matrix. Experimental results demonstrate that successful separation is achieved and compared with conventional frequency-domain BSS methods, it is less computationally complex and has faster convergence

The Application of Blind Source Separation to Feature Decorrelation and Normalizations

We apply a Blind Source Separation BSS algorithm to the decorrelation of Mel-warped cepstra. The observed cepstra are modeled as a convolutive mixture of independent source cepstra. The algorithm aims to minimize a cross-spectral correlation at different lags to reconstruct the source cepstra. Results show that using "independent" cepstra as features leads to a reduction in the WER.Finally, we present three different enhancements to the BSS algorithm. We also present some results of these deviations of the original algorithm

Methods and systems for extracting venous pulsation and respiratory information from photoplethysmographs

A system and method for separating a venous component and an arterial component from a red signal and an infrared signal of a PPG sensor is provided. The method uses the second order statistics of venous and arterial signals to separate the venous andarterial signals. After reliable separation of the venous and thearterial component signals,the component signals can be used for different purposes. In a preferred embodiment, the respiratory signal, pattern, and rate are extracted from the separated venous component and a reliable ?ratio of ratios? is extracted for SpO, using only the arterial component of the PPG signals. The disclosed embodiments enable real-time continuous monitoring of respiration pattern/rate and site-independentarterial oxygen saturation.Board of Regents, University of Texas Syste

A Unifying View on Blind Source Separation of Convolutive Mixtures based on Independent Component Analysis

In many daily-life scenarios, acoustic sources recorded in an enclosure can only be observed with other interfering sources. Hence, convolutive Blind Source Separation (BSS) is a central problem in audio signal processing. Methods based on Independent Component Analysis (ICA) are especially important in this field as they require only few and weak assumptions and allow for blindness regarding the original source signals and the acoustic propagation path. Most of the currently used algorithms belong to one of the following three families: Frequency Domain ICA (FD-ICA), Independent Vector Analysis (IVA), and TRIple-N Independent component analysis for CONvolutive mixtures (TRINICON). While the relation between ICA, FD-ICA and IVA becomes apparent due to their construction, the relation to TRINICON is not well established yet. This paper fills this gap by providing an in-depth treatment of the common building blocks of these algorithms and their differences, and thus provides a common framework for all considered algorithms