500,319 research outputs found
Identifiability for Blind Source Separation of Multiple Finite Alphabet Linear Mixtures
We give under weak assumptions a complete combinatorial characterization of
identifiability for linear mixtures of finite alphabet sources, with unknown
mixing weights and unknown source signals, but known alphabet. This is based on
a detailed treatment of the case of a single linear mixture. Notably, our
identifiability analysis applies also to the case of unknown number of sources.
We provide sufficient and necessary conditions for identifiability and give a
simple sufficient criterion together with an explicit construction to determine
the weights and the source signals for deterministic data by taking advantage
of the hierarchical structure within the possible mixture values. We show that
the probability of identifiability is related to the distribution of a hitting
time and converges exponentially fast to one when the underlying sources come
from a discrete Markov process. Finally, we explore our theoretical results in
a simulation study. Our work extends and clarifies the scope of scenarios for
which blind source separation becomes meaningful
Multi-talker ASR for an unknown number of sources: Joint training of source counting, separation and ASR
Most approaches to multi-talker overlapped speech separation and recognition
assume that the number of simultaneously active speakers is given, but in
realistic situations, it is typically unknown. To cope with this, we extend an
iterative speech extraction system with mechanisms to count the number of
sources and combine it with a single-talker speech recognizer to form the first
end-to-end multi-talker automatic speech recognition system for an unknown
number of active speakers. Our experiments show very promising performance in
counting accuracy, source separation and speech recognition on simulated clean
mixtures from WSJ0-2mix and WSJ0-3mix. Among others, we set a new
state-of-the-art word error rate on the WSJ0-2mix database. Furthermore, our
system generalizes well to a larger number of speakers than it ever saw during
training, as shown in experiments with the WSJ0-4mix database.Comment: 5 pages, INTERSPEECH 202
Deep attractor network for single-microphone speaker separation
Despite the overwhelming success of deep learning in various speech
processing tasks, the problem of separating simultaneous speakers in a mixture
remains challenging. Two major difficulties in such systems are the arbitrary
source permutation and unknown number of sources in the mixture. We propose a
novel deep learning framework for single channel speech separation by creating
attractor points in high dimensional embedding space of the acoustic signals
which pull together the time-frequency bins corresponding to each source.
Attractor points in this study are created by finding the centroids of the
sources in the embedding space, which are subsequently used to determine the
similarity of each bin in the mixture to each source. The network is then
trained to minimize the reconstruction error of each source by optimizing the
embeddings. The proposed model is different from prior works in that it
implements an end-to-end training, and it does not depend on the number of
sources in the mixture. Two strategies are explored in the test time, K-means
and fixed attractor points, where the latter requires no post-processing and
can be implemented in real-time. We evaluated our system on Wall Street Journal
dataset and show 5.49\% improvement over the previous state-of-the-art methods.Comment: 2017 IEEE International Conference on Acoustics, Speech and Signal
Processing (ICASSP
ICAR, a tool for Blind Source Separation using Fourth Order Statistics only
International audienceThe problem of blind separation of overdetermined mixtures of sources, that is, with fewer sources than (or as many sources as) sensors, is addressed in this paper. A new method, named ICAR (Independent Component Analysis using Redundancies in the quadricovariance), is proposed in order to process complex data. This method, without any whitening operation, only exploits some redundancies of a particular quadricovariance matrix of the data. Computer simulations demonstrate that ICAR offers in general good results and even outperforms classical methods in several situations: ICAR ~(i) succeeds in separating sources with low signal to noise ratios, ~(ii) does not require sources with different SO or/and FO spectral densities, ~(iii) is asymptotically not affected by the presence of a Gaussian noise with unknown spatial correlation, (iv) is not sensitive to an over estimation of the number of sources
A Novel Method based on Gaussianity and Sparsity for Signal Separation Algorithms
Blind source separation is a very known problem which refers to finding the original sources without the aid of information about the nature of the sources and the mixing process, to solve this kind of problem having only the mixtures, it is almost impossible , that why using some assumptions is needed in somehow according to the differents situations existing in the real world, for exemple, in laboratory condition, most of tested algorithms works very fine and having good performence because the nature and the number of the input signals are almost known apriori and then the mixing process is well determined for the separation operation. But in fact, the real-life scenario is much more different and of course the problem is becoming much more complicated due to the the fact of having the most of the parameters of the linear equation are unknown. In this paper, we present a novel method based on Gaussianity and Sparsity for signal separation algorithms where independent component analysis will be used. The Sparsity as a preprocessing step, then, as a final step, the Gaussianity based source separation block has been used to estimate the original sources. To validate our proposed method, the FPICA algorithm based on BSS technique has been used
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