343 research outputs found
Overdetermined independent vector analysis
We address the convolutive blind source separation problem for the
(over-)determined case where (i) the number of nonstationary target-sources
is less than that of microphones , and (ii) there are up to
stationary Gaussian noises that need not to be extracted. Independent vector
analysis (IVA) can solve the problem by separating into sources and
selecting the top highly nonstationary signals among them, but this
approach suffers from a waste of computation especially when . Channel
reductions in preprocessing of IVA by, e.g., principle component analysis have
the risk of removing the target signals. We here extend IVA to resolve these
issues. One such extension has been attained by assuming the orthogonality
constraint (OC) that the sample correlation between the target and noise
signals is to be zero. The proposed IVA, on the other hand, does not rely on OC
and exploits only the independence between sources and the stationarity of the
noises. This enables us to develop several efficient algorithms based on block
coordinate descent methods with a problem specific acceleration. We clarify
that one such algorithm exactly coincides with the conventional IVA with OC,
and also explain that the other newly developed algorithms are faster than it.
Experimental results show the improved computational load of the new algorithms
compared to the conventional methods. In particular, a new algorithm
specialized for outperforms the others.Comment: To appear at the 45th International Conference on Acoustics, Speech,
and Signal Processing (ICASSP 2020
Multi-modal Blind Source Separation with Microphones and Blinkies
We propose a blind source separation algorithm that jointly exploits
measurements by a conventional microphone array and an ad hoc array of low-rate
sound power sensors called blinkies. While providing less information than
microphones, blinkies circumvent some difficulties of microphone arrays in
terms of manufacturing, synchronization, and deployment. The algorithm is
derived from a joint probabilistic model of the microphone and sound power
measurements. We assume the separated sources to follow a time-varying
spherical Gaussian distribution, and the non-negative power measurement
space-time matrix to have a low-rank structure. We show that alternating
updates similar to those of independent vector analysis and Itakura-Saito
non-negative matrix factorization decrease the negative log-likelihood of the
joint distribution. The proposed algorithm is validated via numerical
experiments. Its median separation performance is found to be up to 8 dB more
than that of independent vector analysis, with significantly reduced
variability.Comment: Accepted at IEEE ICASSP 2019, Brighton, UK. 5 pages. 3 figure
Inverse-free Online Independent Vector Analysis with Flexible Iterative Source Steering
In this paper, we propose a new online independent vector analysis (IVA)
algorithm for real-time blind source separation (BSS). In many BSS algorithms,
the iterative projection (IP) has been used for updating the demixing matrix, a
parameter to be estimated in BSS. However, it requires matrix inversion, which
can be costly, particularly in online processing. To improve this situation, we
introduce iterative source steering (ISS) to online IVA. ISS does not require
any matrix inversions, and thus its computational complexity is less than that
of IP. Furthermore, when only part of the sources are moving, ISS enables us to
update the demixing matrix flexibly and effectively so that the steering
vectors of only the moving sources are updated. Numerical experiments under a
dynamic condition confirm the efficacy of the proposed method.Comment: 5 pages, 2 figures. Submitted to APSIPA 202
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