Compressive Blind Speech Source Sepration from Underdetermined Mixing Model - 11/04/2023 01:57 EDT
Budget: ₹600 – ₹1,000 INR
Separation of underdetermined mixtures is an important problem in signal
processing that has attracted a great deal of attention over the years. Prior
knowledge is required to solve such problems and one of the most common
forms of structure exploited is sparsity. Another central problem in signal
processing is sampling. Recently, it has been shown that it is possible to
sample well below the Nyquist limit whenever the signal has additional
structure. This theory is known as compressed sensing or compressive
sampling and a wealth of theoretical insight has been gained for signals that
permit a sparse representation. In this paper, the proposed study introduces
undertermined BSS algorithm with unifying frequency texture pattern and
sparse representaion by using a compressed sensing model. First, to
eliminate the infuence of additive white Gaussian noise, a wavelet transform
with tunable Q-factor is used as noise reduction pretreatment. Second, to
obtain an accurate mixing matrix estimation, a blind identifcation method is
designed by identifying single source data. Third, to fnd the best dictionary
to represent the training signals, an arbitrary subset of codewords and the
corresponding coefcients are updated simultaneously. In the source signal
recovery stage, a block processing is used into the mixing signals so that the
source components are separated from each block by using sparse
representation. Then, the whole source signals are reconstructed by
concatenating the separated source components from all the block. The
advantage is reducing the computational complexity. Finally, experimental
results by separating the underdetermined speech mixtures demonstrate the
superiority of the proposed algorithm.
processing that has attracted a great deal of attention over the years. Prior
knowledge is required to solve such problems and one of the most common
forms of structure exploited is sparsity. Another central problem in signal
processing is sampling. Recently, it has been shown that it is possible to
sample well below the Nyquist limit whenever the signal has additional
structure. This theory is known as compressed sensing or compressive
sampling and a wealth of theoretical insight has been gained for signals that
permit a sparse representation. In this paper, the proposed study introduces
undertermined BSS algorithm with unifying frequency texture pattern and
sparse representaion by using a compressed sensing model. First, to
eliminate the infuence of additive white Gaussian noise, a wavelet transform
with tunable Q-factor is used as noise reduction pretreatment. Second, to
obtain an accurate mixing matrix estimation, a blind identifcation method is
designed by identifying single source data. Third, to fnd the best dictionary
to represent the training signals, an arbitrary subset of codewords and the
corresponding coefcients are updated simultaneously. In the source signal
recovery stage, a block processing is used into the mixing signals so that the
source components are separated from each block by using sparse
representation. Then, the whole source signals are reconstructed by
concatenating the separated source components from all the block. The
advantage is reducing the computational complexity. Finally, experimental
results by separating the underdetermined speech mixtures demonstrate the
superiority of the proposed algorithm.