107,548 research outputs found
Separating a Real-Life Nonlinear Image Mixture
When acquiring an image of a paper document, the image printed on the back page sometimes shows through. The mixture of the front- and back-page images thus obtained is markedly nonlinear, and thus constitutes a good real-life test case for nonlinear blind source separation.
This paper addresses a difficult version of this problem, corresponding to the use of "onion skin" paper, which results in a relatively strong nonlinearity of the mixture, which becomes close to singular in the lighter regions of the images. The separation is achieved through the MISEP technique, which is an extension of the well known INFOMAX method. The separation results are assessed with objective quality measures. They show an improvement over the results obtained with linear separation, but have room for further improvement
Overlearning in marginal distribution-based ICA: analysis and solutions
The present paper is written as a word of caution, with users of
independent component analysis (ICA) in mind, to overlearning
phenomena that are often observed.\\
We consider two types of overlearning, typical to high-order
statistics based ICA. These algorithms can be seen to maximise the
negentropy of the source estimates. The first kind of overlearning
results in the generation of spike-like signals, if there are not
enough samples in the data or there is a considerable amount of
noise present. It is argued that, if the data has power spectrum
characterised by curve, we face a more severe problem, which
cannot be solved inside the strict ICA model. This overlearning is
better characterised by bumps instead of spikes. Both overlearning
types are demonstrated in the case of artificial signals as well as
magnetoencephalograms (MEG). Several methods are suggested to
circumvent both types, either by making the estimation of the ICA
model more robust or by including further modelling of the data
A joint separation-classification model for sound event detection of weakly labelled data
Source separation (SS) aims to separate individual sources from an audio
recording. Sound event detection (SED) aims to detect sound events from an
audio recording. We propose a joint separation-classification (JSC) model
trained only on weakly labelled audio data, that is, only the tags of an audio
recording are known but the time of the events are unknown. First, we propose a
separation mapping from the time-frequency (T-F) representation of an audio to
the T-F segmentation masks of the audio events. Second, a classification
mapping is built from each T-F segmentation mask to the presence probability of
each audio event. In the source separation stage, sources of audio events and
time of sound events can be obtained from the T-F segmentation masks. The
proposed method achieves an equal error rate (EER) of 0.14 in SED,
outperforming deep neural network baseline of 0.29. Source separation SDR of
8.08 dB is obtained by using global weighted rank pooling (GWRP) as probability
mapping, outperforming the global max pooling (GMP) based probability mapping
giving SDR at 0.03 dB. Source code of our work is published.Comment: Accepted by ICASSP 201
Dynamic Construction of Stimulus Values in the Ventromedial Prefrontal Cortex
Signals representing the value assigned to stimuli at the time of choice have been repeatedly observed in ventromedial prefrontal cortex (vmPFC). Yet it remains unknown how these value representations are computed from sensory and memory representations in more posterior brain regions. We used electroencephalography (EEG) while subjects evaluated appetitive and aversive food items to study how event-related responses modulated by stimulus value evolve over time. We found that value-related activity shifted from posterior to anterior, and from parietal to central to frontal sensors, across three major time windows after stimulus onset: 150–250 ms, 400–550 ms, and 700–800 ms. Exploratory localization of the EEG signal revealed a shifting network of activity moving from sensory and memory structures to areas associated with value coding, with stimulus value activity localized to vmPFC only from 400 ms onwards. Consistent with these results, functional connectivity analyses also showed a causal flow of information from temporal cortex to vmPFC. Thus, although value signals are present as early as 150 ms after stimulus onset, the value signals in vmPFC appear relatively late in the choice process, and seem to reflect the integration of incoming information from sensory and memory related regions
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