6,529 research outputs found
A multimodal approach to blind source separation of moving sources
A novel multimodal approach is proposed to solve the
problem of blind source separation (BSS) of moving sources. The
challenge of BSS for moving sources is that the mixing filters are
time varying; thus, the unmixing filters should also be time varying,
which are difficult to calculate in real time. In the proposed approach,
the visual modality is utilized to facilitate the separation for
both stationary and moving sources. The movement of the sources
is detected by a 3-D tracker based on video cameras. Positions
and velocities of the sources are obtained from the 3-D tracker
based on a Markov Chain Monte Carlo particle filter (MCMC-PF),
which results in high sampling efficiency. The full BSS solution
is formed by integrating a frequency domain blind source separation
algorithm and beamforming: if the sources are identified
as stationary for a certain minimum period, a frequency domain
BSS algorithm is implemented with an initialization derived from
the positions of the source signals. Once the sources are moving, a
beamforming algorithm which requires no prior statistical knowledge
is used to perform real time speech enhancement and provide
separation of the sources. Experimental results confirm that
by utilizing the visual modality, the proposed algorithm not only
improves the performance of the BSS algorithm and mitigates the
permutation problem for stationary sources, but also provides a
good BSS performance for moving sources in a low reverberant
environment
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.
Source bearing and steering-vector estimation using partially calibrated arrays
The problem of source direction-of-arrival (DOA) estimation using a sensor array is addressed, where some of the sensors are perfectly calibrated, while others are uncalibrated. An algorithm is proposed for estimating the source directions in addition to the estimation of unknown array parameters such as sensor gains and phases, as a way of performing array self-calibration. The cost function is an extension of the maximum likelihood (ML) criteria that were originally developed for DOA estimation with a perfectly calibrated array. A particle swarm optimization (PSO) algorithm is used to explore the high-dimensional problem space and find the global minimum of the cost function. The design of the PSO is a combination of the problem-independent kernel and some newly introduced problem-specific features such as search space mapping, particle velocity control, and particle position clipping. This architecture plus properly selected parameters make the PSO highly flexible and reusable, while being sufficiently specific and effective in the current application. Simulation results demonstrate that the proposed technique may produce more accurate estimates of the source bearings and unknown array parameters in a cheaper way as compared with other popular methods, with the root-mean-squared error (RMSE) approaching and asymptotically attaining the Cramer Rao bound (CRB) even in unfavorable conditions
Non Linear Blind Source Separation Using Different Optimization Techniques
The Independent Component Analysis technique has been used in Blind Source separation of non linear mixtures. The project involves the blind source separation of a non linear mixture of signals based on their mutual independence as the evaluation criteria. The linear mixer is modeled by the Fast ICA algorithm while the Non linear mixer is modeled by an odd polynomial function whose parameters are updated by four separate optimization techniques which are Particle Swarm Optimization, Real coded Genetic Algorithm, Binary Genetic Algorithm and Bacterial Foraging Optimization. The separated mixture outputs of each case was studied and the mean square error in each case was compared giving an idea of the effectiveness of each optimization technique
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