Geometrical interpretation of the PCA subspace method for overdetermined blind source separation

In this paper, we discuss approaches for blind source separation where we can use more sensors than the number of sources for a better performance. The discussion focuses mainly on reducing the dimension of mixed signals before applying independent component analysis. We compare two previously proposed methods. The first is based on principal component analysis, where noise reduction is achieved. The second is based on geometric considerations and selects a subset of sensors according to the fact that a low frequency prefers a wide spacing and a high frequency prefers a narrow spacing. We found that the PCA-based method behaves similarly to the geometry-based method for low frequencies in the way that it emphasizes the outer sensors and yields superior results for high frequencies. Thes

Multimodal methods for blind source separation of audio sources

The enhancement of the performance of frequency domain convolutive blind source separation (FDCBSS) techniques when applied to the problem of separating audio sources recorded in a room environment is the focus of this thesis. This challenging application is termed the cocktail party problem and the ultimate aim would be to build a machine which matches the ability of a human being to solve this task. Human beings exploit both their eyes and their ears in solving this task and hence they adopt a multimodal approach, i.e. they exploit both audio and video modalities. New multimodal methods for blind source separation of audio sources are therefore proposed in this work as a step towards realizing such a machine. The geometry of the room environment is initially exploited to improve the separation performance of a FDCBSS algorithm. The positions of the human speakers are monitored by video cameras and this information is incorporated within the FDCBSS algorithm in the form of constraints added to the underlying cross-power spectral density matrix-based cost function which measures separation performance. [Continues.

Audio Source Separation Using Sparse Representations

This is the author's final version of the article, first published as A. Nesbit, M. G. Jafari, E. Vincent and M. D. Plumbley. Audio Source Separation Using Sparse Representations. In W. Wang (Ed), Machine Audition: Principles, Algorithms and Systems. Chapter 10, pp. 246-264. IGI Global, 2011. ISBN 978-1-61520-919-4. DOI: 10.4018/978-1-61520-919-4.ch010file: NesbitJafariVincentP11-audio.pdf:n\NesbitJafariVincentP11-audio.pdf:PDF owner: markp timestamp: 2011.02.04file: NesbitJafariVincentP11-audio.pdf:n\NesbitJafariVincentP11-audio.pdf:PDF owner: markp timestamp: 2011.02.04The authors address the problem of audio source separation, namely, the recovery of audio signals from recordings of mixtures of those signals. The sparse component analysis framework is a powerful method for achieving this. Sparse orthogonal transforms, in which only few transform coefficients differ significantly from zero, are developed; once the signal has been transformed, energy is apportioned from each transform coefficient to each estimated source, and, finally, the signal is reconstructed using the inverse transform. The overriding aim of this chapter is to demonstrate how this framework, as exemplified here by two different decomposition methods which adapt to the signal to represent it sparsely, can be used to solve different problems in different mixing scenarios. To address the instantaneous (neither delays nor echoes) and underdetermined (more sources than mixtures) mixing model, a lapped orthogonal transform is adapted to the signal by selecting a basis from a library of predetermined bases. This method is highly related to the windowing methods used in the MPEG audio coding framework. In considering the anechoic (delays but no echoes) and determined (equal number of sources and mixtures) mixing case, a greedy adaptive transform is used based on orthogonal basis functions that are learned from the observed data, instead of being selected from a predetermined library of bases. This is found to encode the signal characteristics, by introducing a feedback system between the bases and the observed data. Experiments on mixtures of speech and music signals demonstrate that these methods give good signal approximations and separation performance, and indicate promising directions for future research

Temperature-Driven Anomaly Detection Methods for Structural Health Monitoring

Reported in this thesis is a data-driven anomaly detection method for structural health monitoring which is based on the utilization of temperature-induced variations. Structural anomaly detection should be able to identify meaningful changes in measurements which are due to structural abnormal behaviour. Because, the temperature-induced variations and structural abnormalities may produce significant misinterpretations, the development of solutions to identify a structural anomaly, accounting for temperature influence, from measurements, is a critical procedure to support structural maintenance. A temperature-driven anomaly detection method is proposed, that introduces the idea of blind source separation for extracting thermal response and for further anomaly detection. Two thermal feature extraction methods are employed corresponding to the classification of underdetermined and overdetermined methods. The underdetermined method has the three phases of: (a) mode decomposition by utilising Empirical Mode Decomposition or Ensemble Empirical Mode Decomposition; (b) data reduction by performing Principal Component Analysis (PCA); (c) blind separation by applying Independent Component Analysis (ICA). The overdetermined method has the two stages of the pre-indication according to PCA and the blind separation by the devotion of ICA. Based on the extracted thermal response, the temperature-driven anomaly detection method is later developed in combination with the four methodologies of: Moving Principal Component Analysis (MPCA); Robust Regression Analysis (RRA); One-Class Support Vector Machine (OCSVM); Artificial Neural Network (ANN). Therefore, the proposed temperature-driven anomaly detection methods are designed as Td-MPCA, Td-RRA, Td-OCSVM, and Td-ANN. The proposed thermal feature extraction methods and temperature-driven anomaly detection methods have been investigated in the context of three case studies. The first case is a numerical truss bridge with simulated material stiffness reduction to create levels of damage. The second case is a purpose constructed truss bridge in the Structures Lab at the University of Warwick. The third case study is Ricciolo curved viaduct in Switzerland. Two primary findings can be confirmed from the evaluation results of these three case studies. Firstly, temperature-induced variations can conceal damage information in measurements. Secondly, the detection abilities of temperature-driven methods, which are Td-MPCA, Td-RRA, Td-OCSVM, and Td-ANN, for disclosing slight anomalies in time are more efficient when compared with the current anomaly detection method, which are MPCA, RRA, OCSVM, and ANN. The unique features of the author’s proposed temperature-driven anomaly detection method can be highlighted as follows: (a) it is a data-driven method for extracting features from an unknown structural system. In another word, the prior knowledge of the structural in-service conditions and physical models are not necessary; (b) it is the first time that blind source separation approaches and relative algorithms have been successfully employed for extracting temperature-induced responses; (c) it is a new approach to reliably assess the capability of using temperature-induced responses for anomaly detection

Over-Determined Source Separation and Localization Using Distributed Microphones

This work was supported by the U.K. Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/K007491/1

Blind Source Separation and Localization Using Microphone Arrays

The blind source separation and localization problem for audio signals is studied using microphone arrays. Pure delay mixtures of source signals typically encountered in outdoor environments are considered. Our proposed approach utilizes the subspace methods, including multiple signal classification (MUSIC) and estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithms, to estimate the directions of arrival (DOAs) of the sources from the collected mixtures. Since audio signals are generally considered broadband, the DOA estimates at frequencies with the large sum of squared amplitude values are combined to obtain the final DOA estimates. Using the estimated DOAs, the corresponding mixing and demixing matrices are computed, and the source signals are recovered using the inverse short time Fourier transform. Subspace methods take advantage of the spatial covariance matrix of the collected mixtures to achieve robustness to noise. While the subspace methods have been studied for localizing radio frequency signals, audio signals have their special properties. For instance, they are nonstationary, naturally broadband and analog. All of these make the separation and localization for the audio signals more challenging. Moreover, our algorithm is essentially equivalent to the beamforming technique, which suppresses the signals in unwanted directions and only recovers the signals in the estimated DOAs. Several crucial issues related to our algorithm and their solutions have been discussed, including source number estimation, spatial aliasing, artifact filtering, different ways of mixture generation, and source coordinate estimation using multiple arrays. Additionally, comprehensive simulations and experiments have been conducted to examine various aspects of the algorithm. Unlike the existing blind source separation and localization methods, which are generally time consuming, our algorithm needs signal mixtures of only a short duration and therefore supports real-time implementation.School of Materials Science & Engineerin

Localization of brain signal sources using blind source separation

Reliable localization of brain signal sources by using convenient, easy, and hazardless data acquisition techniques can potentially play a key role in the understanding, analysis, and tracking of brain activities for determination of physiological, pathological, and functional abnormalities. The sources can be due to normal brain activities, mental disorders, stimulation of the brain, or movement related tasks. The focus of this thesis is therefore the development of novel source localization techniques based upon EEG measurements. Independent component analysis is used in blind separation (BSS) of the EEG sources to yield three different approaches for source localization. In the first method the sources are localized over the scalp pattern using BSS in various subbands, and by investigating the number of components which are likely to be the true sources. In the second method, the sources are separated and their corresponding topographical information is used within a least-squares algorithm to localize the sources within the brain region. The locations of the known sources, such as some normal brain rhythms, are also utilized to help in determining the unknown sources. The final approach is an effective BSS algorithm partially constrained by information related to the known sources. In addition, some investigation have been undertaken to incorporate non-homogeneity of the head layers in terms of the changes in electrical and magnetic characteristics and also with respect to the noise level within the processing methods. Experimental studies with real and synthetic data sets are undertaken using MATLAB and the efficacy of each method discussed