Using multiple frequency bins for stabilization of FD-ICA algorithms

In the frequency domain independent component analysis approaches for audiosources separation, the convolutive mixing problem is replaced by thesolution of several instantaneous mixing problems, one for each frequencybin of the short time Fourier transform. This methodology yields good resultsbut requires the solution of the permutation ambiguity. Moreover, theperformance of the separation algorithms for each bin is not guaranteed tobe equivalent, thus some bins can have worse results than others. In thispaper a technique based on data from multiple bins is proposed to addressthese issues. The use of multiple bin information produces a coupling of theseparation, resulting in more stable separation matrices and reducing the occurrence of permutations, but increasing in computational cost. This can bemitigated by a sub sampling of the multiple bins information. The resultsshow that both approaches are beneficial for the frequency domain ICA approach,producing better separation in terms of objective quality measures.Fil: Di Persia, Leandro Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentin

Independent Component Analysis: Blind Source Separation

Independent Component Analysis (ICA) is a technique used since middle 80s, and due to all its applications, it has been a common research topic. Simplifying the concept, with the ICA technique we can separate multivariate additive signals. Despite that there are other methods to do so, ICA can do it without knowing nothing (or barely nothing) of the signals and context. Along this thesis the basic algorithm for Independent Component Analysis will be explained. It is called FastICA and was invented by Aapo Hyvärinen as a simply and versatile algorithm with a scheme of fixed-point iterations. This means an algorithm that search the convergence of a vector with iterations, similar to the Newton’s method. This technique is not that simple though, the mathematic and theoretical background is quite complex. But in order to understand how the algorithm works, all of the concepts will be explained step by step.Boluda Burguete, V. (2015). Independent Component Analysis: Blind Source Separation. Universitat Politècnica de València. http://hdl.handle.net/10251/57400TFG

Efficient Multiband Algorithms for Blind Source Separation

The problem of blind separation refers to recovering original signals, called source signals, from the mixed signals, called observation signals, in a reverberant environment. The mixture is a function of a sequence of original speech signals mixed in a reverberant room. The objective is to separate mixed signals to obtain the original signals without degradation and without prior information of the features of the sources. The strategy used to achieve this objective is to use multiple bands that work at a lower rate, have less computational cost and a quicker convergence than the conventional scheme. Our motivation is the competitive results of unequal-passbands scheme applications, in terms of the convergence speed. The objective of this research is to improve unequal-passbands schemes by improving the speed of convergence and reducing the computational cost. The first proposed work is a novel maximally decimated unequal-passbands scheme.This scheme uses multiple bands that make it work at a reduced sampling rate, and low computational cost. An adaptation approach is derived with an adaptation step that improved the convergence speed. The performance of the proposed scheme was measured in different ways. First, the mean square errors of various bands are measured and the results are compared to a maximally decimated equal-passbands scheme, which is currently the best performing method. The results show that the proposed scheme has a faster convergence rate than the maximally decimated equal-passbands scheme. Second, when the scheme is tested for white and coloured inputs using a low number of bands, it does not yield good results; but when the number of bands is increased, the speed of convergence is enhanced. Third, the scheme is tested for quick changes. It is shown that the performance of the proposed scheme is similar to that of the equal-passbands scheme. Fourth, the scheme is also tested in a stationary state. The experimental results confirm the theoretical work. For more challenging scenarios, an unequal-passbands scheme with over-sampled decimation is proposed; the greater number of bands, the more efficient the separation. The results are compared to the currently best performing method. Second, an experimental comparison is made between the proposed multiband scheme and the conventional scheme. The results show that the convergence speed and the signal-to-interference ratio of the proposed scheme are higher than that of the conventional scheme, and the computation cost is lower than that of the conventional scheme

Robust Automatic Speech Recognition through On-line Semi Blind Signal Extraction

This paper describes the system used to process the data of the CHiME Pascal 2011 competition, whose goal is to separate the desired speech and recognize the commands being spoken. The binaural recorded mixtures are processed by an on-line Semi- Blind Source Extraction algorithm. The algorithm is based on a multi-stage architecture combining the advantages of con- strained Independent Component Analysis and Wiener-based processing, allowing the estimation of the target signal with lim- ited distortion. The recovered target signal is then fed to the rec- ognizer which uses noise robust features based on Gammatone Frequency Cepstral Coefficients. Moreover, model adaptation to actual processing is applied as a further stage to reduce the acoustic mismatch. Performance comparison between differ- ent model/algorithmic settings is reported for both development and test data sets

Enhanced independent vector analysis for audio separation in a room environment

Independent vector analysis (IVA) is studied as a frequency domain blind source separation method, which can theoretically avoid the permutation problem by retaining the dependency between different frequency bins of the same source vector while removing the dependency between different source vectors. This thesis focuses upon improving the performance of independent vector analysis when it is used to solve the audio separation problem in a room environment. A specific stability problem of IVA, i.e. the block permutation problem, is identified and analyzed. Then a robust IVA method is proposed to solve this problem by exploiting the phase continuity of the unmixing matrix. Moreover, an auxiliary function based IVA algorithm with an overlapped chain type source prior is proposed as well to mitigate this problem. Then an informed IVA scheme is proposed which combines the geometric information of the sources from video to solve the problem by providing an intelligent initialization for optimal convergence. The proposed informed IVA algorithm can also achieve a faster convergence in terms of iteration numbers and better separation performance. A pitch based evaluation method is defined to judge the separation performance objectively when the information describing the mixing matrix and sources is missing. In order to improve the separation performance of IVA, an appropriate multivariate source prior is needed to better preserve the dependency structure within the source vectors. A particular multivariate generalized Gaussian distribution is adopted as the source prior. The nonlinear score function derived from this proposed source prior contains the fourth order relationships between different frequency bins, which provides a more informative and stronger dependency structure compared with the original IVA algorithm and thereby improves the separation performance. Copula theory is a central tool to model the nonlinear dependency structure. The t copula is proposed to describe the dependency structure within the frequency domain speech signals due to its tail dependency property, which means if one variable has an extreme value, other variables are expected to have extreme values. A multivariate student's t distribution constructed by using a t copula with the univariate student's t marginal distribution is proposed as the source prior. Then the IVA algorithm with the proposed source prior is derived. The proposed algorithms are tested with real speech signals in different reverberant room environments both using modelled room impulse response and real room recordings. State-of-the-art criteria are used to evaluate the separation performance, and the experimental results confirm the advantage of the proposed algorithms

An Online Solution for Localisation, Tracking and Separation of Moving Speech Sources

The problem of separating a time varying number of speech sources in a room is difficult to solve. The challenge lies in estimating the number and the location of these speech sources. Furthermore, the tracked speech sources need to be separated. This thesis proposes a solution which utilises the Random Finite Set approach to estimate the number and location of these speech sources and subsequently separate the speech source mixture via time frequency masking