1,489 research outputs found
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
Real-time Sound Source Separation For Music Applications
Sound source separation refers to the task of extracting individual sound sources from some number of mixtures of those sound sources. In this thesis, a novel sound source separation algorithm for musical applications is presented. It leverages the fact that the vast majority of commercially recorded music since the 1950s has been mixed down for two channel reproduction, more commonly known as stereo. The algorithm presented in Chapter 3 in this thesis requires no prior knowledge or learning and performs the task of separation based purely on azimuth discrimination within the stereo field. The algorithm exploits the use of the pan pot as a means to achieve image localisation within stereophonic recordings. As such, only an interaural intensity difference exists between left and right channels for a single source. We use gain scaling and phase cancellation techniques to expose frequency dependent nulls across the azimuth domain, from which source separation and resynthesis is carried out. The algorithm is demonstrated to be state of the art in the field of sound source separation but also to be a useful pre-process to other tasks such as music segmentation and surround sound upmixing
Simultaneous diagonalisation of the covariance and complementary covariance matrices in quaternion widely linear signal processing
Recent developments in quaternion-valued widely linear processing have
established that the exploitation of complete second-order statistics requires
consideration of both the standard covariance and the three complementary
covariance matrices. Although such matrices have a tremendous amount of
structure and their decomposition is a powerful tool in a variety of
applications, the non-commutative nature of the quaternion product has been
prohibitive to the development of quaternion uncorrelating transforms. To this
end, we introduce novel techniques for a simultaneous decomposition of the
covariance and complementary covariance matrices in the quaternion domain,
whereby the quaternion version of the Takagi factorisation is explored to
diagonalise symmetric quaternion-valued matrices. This gives new insights into
the quaternion uncorrelating transform (QUT) and forms a basis for the proposed
quaternion approximate uncorrelating transform (QAUT) which simultaneously
diagonalises all four covariance matrices associated with improper quaternion
signals. The effectiveness of the proposed uncorrelating transforms is
validated by simulations on both synthetic and real-world quaternion-valued
signals.Comment: 41 pages, single column, 10 figure
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