181 research outputs found
Information-Thermodynamic Bound on Information Flow in Turbulent Cascade
We investigate the nature of information flow in turbulence from an
information-thermodynamic viewpoint. For the fully developed three-dimensional
fluid turbulence described by the fluctuating Navier-Stokes equation, we prove
that information of large-scale eddies is transferred to small scales along
with the energy cascade. We numerically illustrate our findings using a shell
model and further show that in the inertial range, the intensity of the
information flow is nearly constant and can be scaled by the large-eddy
turnover time. Our numerical results also suggest that the corresponding
information-thermodynamic efficiency is quite low compared to other typical
information processing systems such as Maxwell's demon. These findings provide
a new perspective on how universality and intermittency of turbulent
fluctuations emerge at small scales.Comment: 18 pages, 5 figures. In ver.5, we have extended the main results. The
presentation has also been revised substantiall
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
Simultaneous Spinal and Intracranial Chronic Subdural Hematoma Cured by Craniotomy and Laminectomy: A Video Case Report
Subjective intelligibility of speech sounds enhanced by ideal ratio mask via crowdsourced remote experiments with effective data screening
It is essential to perform speech intelligibility (SI) experiments with human
listeners to evaluate the effectiveness of objective intelligibility measures.
Recently crowdsourced remote testing has become popular to collect a massive
amount and variety of data with relatively small cost and in short time.
However, careful data screening is essential for attaining reliable SI data. We
compared the results of laboratory and crowdsourced remote experiments to
establish an effective data screening technique. We evaluated the SI of noisy
speech sounds enhanced by a single-channel ideal ratio mask (IRM) and
multi-channel mask-based beamformers. The results demonstrated that the SI
scores were improved by these enhancement methods. In particular, the
IRM-enhanced sounds were much better than the unprocessed and other enhanced
sounds, indicating IRM enhancement may give the upper limit of speech
enhancement performance. Moreover, tone pip tests, for which participants were
asked to report the number of audible tone pips, reduced the variability of
crowdsourced remote results so that the laboratory results became similar. Tone
pip tests could be useful for future crowdsourced experiments because of their
simplicity and effectiveness for data screening.Comment: This paper was submitted to Interspeech 2022
(http://www.interspeech2022.org
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