Iterative issues of ICA, quality of separation and number of sources : a study for biosignal applications

This thesis has evaluated the use of Independent Component Analysis (ICA) on Surface Electromyography (sEMG), focusing on the biosignal applications. This research has identified and addressed the following four issues related to the use of ICA for biosignals: • The iterative nature of ICA • The order and magnitude ambiguity problems of ICA • Estimation of number of sources based on dependency and independency nature of the signals • Source separation for non-quadratic ICA (undercomplete and overcomplete) This research first establishes the applicability of ICA for sEMG and also identifies the shortcomings related to order and magnitude ambiguity. It has then developed, a mitigation strategy for these issues by using a single unmixing matrix and neural network weight matrix corresponding to the specific user. The research reports experimental verification of the technique and also the investigation of the impact of inter-subject and inter-experimental variations. The results demonstrate that while using sEMG without separation gives only 60% accuracy, and sEMG separated using traditional ICA gives an accuracy of 65%, this approach gives an accuracy of 99% for the same experimental data. Besides the marked improvement in accuracy, the other advantages of such a system are that it is suitable for real time operations and is easy to train by a lay user. The second part of this thesis reports research conducted to evaluate the use of ICA for the separation of bioelectric signals when the number of active sources may not be known. The work proposes the use of value of the determinant of the Global matrix generated using sparse sub band ICA for identifying the number of active sources. The results indicate that the technique is successful in identifying the number of active muscles for complex hand gestures. The results support the applications such as human computer interface. This thesis has also developed a method of determining the number of independent sources in a given mixture and has also demonstrated that using this information, it is possible to separate the signals in an undercomplete situation and reduce the redundancy in the data using standard ICA methods. The experimental verification has demonstrated that the quality of separation using this method is better than other techniques such as Principal Component Analysis (PCA) and selective PCA. This has number of applications such as audio separation and sensor networks

Bounded Support Finite Mixtures for Multidimensional Data Modeling and Clustering

Data is ever increasing with today’s many technological advances in terms of both quantity and dimensions. Such inflation has posed various challenges in statistical and data analysis methods and hence requires the development of new powerful models for transforming the data into useful information. Therefore, it was necessary to explore and develop new ideas and techniques to keep pace with challenging learning applications in data analysis, modeling and pattern recognition. Finite mixture models have received considerable attention due to their ability to effectively and efficiently model high dimensional data. In mixtures, choice of distribution is a critical issue and it has been observed that in many real life applications, data exist in a bounded support region, whereas distributions adopted to model the data lie in unbounded support regions. Therefore, it was proposed to define bounded support distributions in mixtures and introduce a modified procedure for parameters estimation by considering the bounded support of underlying distributions. The main goal of this thesis is to introduce bounded support mixtures, their parameters estimation, automatic determination of number of mixture components and application of mixtures in feature extraction techniques to overall improve the learning pipeline. Five different unbounded support distributions are selected for applying the idea of bounded support mixtures and modified parameters estimation using maximum likelihood via Expectation-Maximization (EM). Probability density functions selected for this thesis include Gaussian, Laplace, generalized Gaussian, asymmetric Gaussian and asymmetric generalized Gaussian distributions, which are chosen due to their flexibility and broad applications in speech and image processing. The proposed bounded support mixtures are applied in various speech and images datasets to create leaning applications to demonstrate the effectiveness of proposed approach. Mixtures of bounded Gaussian and bounded Laplace are also applied in feature extraction and data representation techniques, which further improves the learning and modeling capability of underlying models. The proposed feature representation via bounded support mixtures is applied in both speech and images datasets to examine its performance. Automatic selection of number of mixture components is very important in clustering and parameter learning is highly dependent on model selection and it is proposed for mixture of bounded Gaussian and bounded asymmetric generalized Gaussian using minimum message length. Proposed model selection criterion and parameter learning are simultaneously applied in speech and images datasets for both models to examine the model selection performance in clustering

Guided Matching Pursuit and its Application to Sound Source Separation

In the last couple of decades there has been an increasing interest in the application of source separation technologies to musical signal processing. Given a signal that consists of a mixture of musical sources, source separation aims at extracting and/or isolating the signals that correspond to the original sources. A system capable of high quality source separation could be an invaluable tool for the sound engineer as well as the end user. Applications of source separation include, but are not limited to, remixing, up-mixing, spatial re-configuration, individual source modification such as filtering, pitch detection/correction and time stretching, music transcription, voice recognition and source-specific audio coding to name a few. Of particular interest is the problem of separating sources from a mixture comprising two channels (2.0 format) since this is still the most commonly used format in the music industry and most domestic listening environments. When the number of sources is greater than the number of mixtures (which is usually the case with stereophonic recordings) then the problem of source separation becomes under-determined and traditional source separation techniques, such as “Independent Component Analysis” (ICA) cannot be successfully applied. In such cases a family of techniques known as “Sparse Component Analysis” (SCA) are better suited. In short a mixture signal is decomposed into a new domain were the individual sources are sparsely represented which implies that their corresponding coefficients will have disjoint (or almost) disjoint supports. Taking advantage of this property along with the spatial information within the mixture and other prior information that could be available, it is possible to identify the sources in the new domain and separate them by going back to the time domain. It is a fact that sparse representations lead to higher quality separation. Regardless, the most commonly used front-end for a SCA system is the ubiquitous short-time Fourier transform (STFT) which although is a sparsifying transform it is not the best choice for this job. A better alternative is the matching pursuit (MP) decomposition. MP is an iterative algorithm that decomposes a signal into a set of elementary waveforms called atoms chosen from an over-complete dictionary in such a way so that they represent the inherent signal structures. A crucial part of MP is the creation of the dictionary which directly affects the results of the decomposition and subsequently the quality of source separation. Selecting an appropriate dictionary could prove a difficult task and an adaptive approach would be appropriate. This work proposes a new MP variant termed guided matching pursuit (GMP) which adds a new pre-processing step into the main sequence of the MP algorithm. The purpose of this step is to perform an analysis of the signal and extract important features, termed guide maps, that are used to create dynamic mini-dictionaries comprising atoms which are expected to correlate well with the underlying signal structures thus leading to focused and more efficient searches around particular supports of the signal. This algorithm is accompanied by a modular and highly flexible MATLAB implementation which is suited to the processing of long duration audio signals. Finally the new algorithm is applied to the source separation of two-channel linear instantaneous mixtures and preliminary testing demonstrates that the performance of GMP is on par with the performance of state of the art systems

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Content-Style Decomposition: Representation Discovery and Applications

Content-style decompositions, or CSDs, decompose entities into content, defined by the entity's class, and style, defined as the remaining within-class variation. Content is typically defined in terms of some task. For example, in a face recognition task, identity is the content; in an emotion recognition task, expression is the content. CSDs have many applications: they can provide insight into domains where we have little prior knowledge of the sources of within- and between-class variation, and content-style recombinations are interesting as a creative exercise or for data set augmentation. Our approach is to decompose CSD discovery into two sub-problems: (1) to find useful representations of content that capture the class structure of the domain, and (2) to use those content-representations to discover CSDs. We make contributions to both sub-problems. First, we propose the F-statistic loss, a new method for discovering content representations that uses statistics of class separation on isolated embedding dimensions within a minibatch to determine when to terminate training. In addition to state-of-the-art performance on few-shot learning, we find that the method leads to factorial (also known as disentangled) representations of content when applied with a novel form of weak supervision. Previous work on disentangling is either unsupervised or uses a factor-aware oracle, which provides similar/dissimilar judgments with respect to a named attribute/factor. We explore an intermediate form of supervision, an unnamed-factor oracle, which provides similarity judgments with respect to a random unnamed factor. We demonstrate that the F-statistic loss leads to better disentangling when compared with other deep-embeddings losses and β-VAE, a state-of-the-art unsupervised disentangling method. Second, we introduce a new loss for discovering CSDs that can arbitrarily recombine content and style, called leakage filtering. In contrast to previous research which attempts to separate content and style in two different representation vectors, leakage filtering allows for imperfectly disentangled representations but ensures that residual content information will not leak out of the style representation and vice versa. Leakage filtering is also distinguished by its ability to operate on novel content-classes and by its lack of dependency on style labels for training. The recombined images produced are of high quality and can be used to augment datasets for few-shot learning tasks, yielding significant generalization improvements

Models and analysis of vocal emissions for biomedical applications

This book of Proceedings collects the papers presented at the 3rd International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2003, held 10-12 December 2003, Firenze, Italy. The workshop is organised every two years, and aims to stimulate contacts between specialists active in research and industrial developments, in the area of voice analysis for biomedical applications. The scope of the Workshop includes all aspects of voice modelling and analysis, ranging from fundamental research to all kinds of biomedical applications and related established and advanced technologies

Robust speech recognition with spectrogram factorisation

Communication by speech is intrinsic for humans. Since the breakthrough of mobile devices and wireless communication, digital transmission of speech has become ubiquitous. Similarly distribution and storage of audio and video data has increased rapidly. However, despite being technically capable to record and process audio signals, only a fraction of digital systems and services are actually able to work with spoken input, that is, to operate on the lexical content of speech. One persistent obstacle for practical deployment of automatic speech recognition systems is inadequate robustness against noise and other interferences, which regularly corrupt signals recorded in real-world environments. Speech and diverse noises are both complex signals, which are not trivially separable. Despite decades of research and a multitude of different approaches, the problem has not been solved to a sufficient extent. Especially the mathematically ill-posed problem of separating multiple sources from a single-channel input requires advanced models and algorithms to be solvable. One promising path is using a composite model of long-context atoms to represent a mixture of non-stationary sources based on their spectro-temporal behaviour. Algorithms derived from the family of non-negative matrix factorisations have been applied to such problems to separate and recognise individual sources like speech. This thesis describes a set of tools developed for non-negative modelling of audio spectrograms, especially involving speech and real-world noise sources. An overview is provided to the complete framework starting from model and feature definitions, advancing to factorisation algorithms, and finally describing different routes for separation, enhancement, and recognition tasks. Current issues and their potential solutions are discussed both theoretically and from a practical point of view. The included publications describe factorisation-based recognition systems, which have been evaluated on publicly available speech corpora in order to determine the efficiency of various separation and recognition algorithms. Several variants and system combinations that have been proposed in literature are also discussed. The work covers a broad span of factorisation-based system components, which together aim at providing a practically viable solution to robust processing and recognition of speech in everyday situations

Speech dereverberation and speaker separation using microphone arrays in realistic environments

This thesis concentrates on comparing novel and existing dereverberation and speaker separation techniques using multiple corpora, including a new corpus collected using a microphone array. Many corpora currently used for these techniques are recorded using head-mounted microphones in anechoic chambers. This novel corpus contains recordings with noise and reverberation made in office and workshop environments. Novel algorithms present a different way of approximating the reverberation, producing results that are competitive with existing algorithms. Dereverberation is evaluated using seven correlation-based algorithms and applied to two different corpora. Three of these are novel algorithms (Hs NTF, Cauchy WPE and Cauchy MIMO WPE). Both non-learning and learning algorithms are tested, with the learning algorithms performing better. For single and multi-channel speaker separation, unsupervised non-negative matrix factorization (NMF) algorithms are compared using three cost functions combined with sparsity, convolution and direction of arrival. The results show that the choice of cost function is important for improving the separation result. Furthermore, six different supervised deep learning algorithms are applied to single channel speaker separation. Historic information improves the result. When comparing NMF to deep learning, NMF is able to converge faster to a solution and provides a better result for the corpora used in this thesis