Enhanced IVA for audio separation in highly reverberant environments

Blind Audio Source Separation (BASS), inspired by the "cocktail-party problem", has been a leading research application for blind source separation (BSS). This thesis concerns the enhancement of frequency domain convolutive blind source separation (FDCBSS) techniques for audio separation in highly reverberant room environments. Independent component analysis (ICA) is a higher order statistics (HOS) approach commonly used in the BSS framework. When applied to audio FDCBSS, ICA based methods suffer from the permutation problem across the frequency bins of each source. Independent vector analysis (IVA) is an FD-BSS algorithm that theoretically solves the permutation problem by using a multivariate source prior, where the sources are considered to be random vectors. The algorithm allows independence between multivariate source signals, and retains dependency between the source signals within each source vector. The source prior adopted to model the nonlinear dependency structure within the source vectors is crucial to the separation performance of the IVA algorithm. The focus of this thesis is on improving the separation performance of the IVA algorithm in the application of BASS. An alternative multivariate Student's t distribution is proposed as the source prior for the batch IVA algorithm. A Student's t probability density function can better model certain frequency domain speech signals due to its tail dependency property. Then, the nonlinear score function, for the IVA, is derived from the proposed source prior. A novel energy driven mixed super Gaussian and Student's t source prior is proposed for the IVA and FastIVA algorithms. The Student's t distribution, in the mixed source prior, can model the high amplitude data points whereas the super Gaussian distribution can model the lower amplitude information in the speech signals. The ratio of both distributions can be adjusted according to the energy of the observed mixtures to adapt for different types of speech signals. A particular multivariate generalized Gaussian distribution is adopted as the source prior for the online IVA algorithm. The nonlinear score function derived from this proposed source prior contains fourth order relationships between different frequency bins, which provides a more informative and stronger dependency structure and thereby improves the separation performance. An adaptive learning scheme is developed to improve the performance of the online IVA algorithm. The scheme adjusts the learning rate as a function of proximity to the target solutions. The scheme is also accompanied with a novel switched source prior technique taking the best performance properties of the super Gaussian source prior and the generalized Gaussian source prior as the algorithm converges. The methods and techniques, proposed in this thesis, are evaluated with real speech source signals in different simulated and real reverberant acoustic environments. A variety of measures are used within the evaluation criteria of the various algorithms. The experimental results demonstrate improved performance of the proposed methods and their robustness in a wide range of situations

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

A dynamic latent variable model for source separation

We propose a novel latent variable model for learning latent bases for time-varying non-negative data. Our model uses a mixture multinomial as the likelihood function and proposes a Dirichlet distribution with dynamic parameters as a prior, which we call the dynamic Dirichlet prior. An expectation maximization (EM) algorithm is developed for estimating the parameters of the proposed model. Furthermore, we connect our proposed dynamic Dirichlet latent variable model (dynamic DLVM) to the two popular latent basis learning methods - probabilistic latent component analysis (PLCA) and non-negative matrix factorization (NMF). We show that (i) PLCA is a special case of the dynamic DLVM, and (ii) dynamic DLVM can be interpreted as a dynamic version of NMF. The effectiveness of the proposed model is demonstrated through extensive experiments on speaker source separation, and speech-noise separation. In both cases, our method performs better than relevant and competitive baselines. For speaker separation, dynamic DLVM shows 1.38 dB improvement in terms of source to interference ratio, and 1 dB improvement in source to artifact ratio

Statistical single channel source separation

PhD ThesisSingle channel source separation (SCSS) principally is one of the challenging fields in signal processing and has various significant applications. Unlike conventional SCSS methods which were based on linear instantaneous model, this research sets out to investigate the separation of single channel in two types of mixture which is nonlinear instantaneous mixture and linear convolutive mixture. For the nonlinear SCSS in instantaneous mixture, this research proposes a novel solution based on a two-stage process that consists of a Gaussianization transform which efficiently compensates for the nonlinear distortion follow by a maximum likelihood estimator to perform source separation. For linear SCSS in convolutive mixture, this research proposes new methods based on nonnegative matrix factorization which decomposes a mixture into two-dimensional convolution factor matrices that represent the spectral basis and temporal code. The proposed factorization considers the convolutive mixing in the decomposition by introducing frequency constrained parameters in the model. The method aims to separate the mixture into its constituent spectral-temporal source components while alleviating the effect of convolutive mixing. In addition, family of Itakura-Saito divergence has been developed as a cost function which brings the beneficial property of scale-invariant. Two new statistical techniques are proposed, namely, Expectation-Maximisation (EM) based algorithm framework which maximizes the log-likelihood of a mixed signals, and the maximum a posteriori approach which maximises the joint probability of a mixed signal using multiplicative update rules. To further improve this research work, a novel method that incorporates adaptive sparseness into the solution has been proposed to resolve the ambiguity and hence, improve the algorithm performance. The theoretical foundation of the proposed solutions has been rigorously developed and discussed in details. Results have concretely shown the effectiveness of all the proposed algorithms presented in this thesis in separating the mixed signals in single channel and have outperformed others available methods.Universiti Teknikal Malaysia Melaka(UTeM), Ministry of Higher Education of Malaysi

Dirichlet latent variable model : a dynamic model based on Dirichlet prior for audio processing

We propose a dynamic latent variable model for learning latent bases from time varying, non-negative data. We take a probabilistic approach to modeling the temporal dependence in data by introducing a dynamic Dirichlet prior – a Dirichlet distribution with dynamic parameters. This new distribution allows us to assure non-negativity and avoid intractability when sequential updates are performed (otherwise encountered in using Dirichlet prior). We refer to the proposed model as the Dirichlet latent variable model (DLVM). We develop an expectation maximization algorithm for the proposed model, and also derive a maximum a posteriori estimate of the parameters. Furthermore, we connect the proposed DLVM to two popular latent basis learning methods - probabilistic latent component analysis (PLCA) and non-negative matrix factorization (NMF).We show that (i) PLCA is a special case of our DLVM, and (ii) DLVM can be interpreted as a dynamic version of NMF. The usefulness of DLVM is demonstrated for three audio processing applications - speaker source separation, denoising, and bandwidth expansion. To this end, a new algorithm for source separation is also proposed. Through extensive experiments on benchmark databases, we show that the proposed model out performs several relevant existing methods in all three applications

Online source separation in reverberant environments exploiting known speaker locations

This thesis concerns blind source separation techniques using second order statistics and higher order statistics for reverberant environments. A focus of the thesis is algorithmic simplicity with a view to the algorithms being implemented in their online forms. The main challenge of blind source separation applications is to handle reverberant acoustic environments; a further complication is changes in the acoustic environment such as when human speakers physically move. A novel time-domain method which utilises a pair of finite impulse response filters is proposed. The method of principle angles is defined which exploits a singular value decomposition for their design. The pair of filters are implemented within a generalised sidelobe canceller structure, thus the method can be considered as a beamforming method which cancels one source. An adaptive filtering stage is then employed to recover the remaining source, by exploiting the output of the beamforming stage as a noise reference. A common approach to blind source separation is to use methods that use higher order statistics such as independent component analysis. When dealing with realistic convolutive audio and speech mixtures, processing in the frequency domain at each frequency bin is required. As a result this introduces the permutation problem, inherent in independent component analysis, across the frequency bins. Independent vector analysis directly addresses this issue by modeling the dependencies between frequency bins, namely making use of a source vector prior. An alternative source prior for real-time (online) natural gradient independent vector analysis is proposed. A Student's t probability density function is known to be more suited for speech sources, due to its heavier tails, and is incorporated into a real-time version of natural gradient independent vector analysis. The final algorithm is realised as a real-time embedded application on a floating point Texas Instruments digital signal processor platform. Moving sources, along with reverberant environments, cause significant problems in realistic source separation systems as mixing filters become time variant. A method which employs the pair of cancellation filters, is proposed to cancel one source coupled with an online natural gradient independent vector analysis technique to improve average separation performance in the context of step-wise moving sources. This addresses `dips' in performance when sources move. Results show the average convergence time of the performance parameters is improved. Online methods introduced in thesis are tested using impulse responses measured in reverberant environments, demonstrating their robustness and are shown to perform better than established methods in a variety of situations