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Sound event detection is the task of identifying automatically the presence and temporal boundaries of sound events within an input audio stream. In the last years, deep learning methods have established themselves as the state-of-the-art approach for the task, using binary indicators during training to denote whether an event is active or inactive. However, such binary activity indicators do not fully describe the events, and estimating the envelope of the sounds could provide more precise modeling of their activity.

The combination of coordinated multi-point (CoMP) and underlay spectrum sharing promises substantial spectral efficiency (SE) gains for future cellular networks. However, this concept has been largely overlooked in the literature. Moreover, none of the few relevant studies consider the use of “standard” transmission strategies to facilitate the adoption of the aforementioned communication paradigm by 5G networks.

The combination of coordinated multi-point (CoMP) and underlay spectrum sharing promises substantial spectral efficiency (SE) gains for future cellular networks. However, this concept has been largely overlooked in the literature. Moreover, none of the few relevant studies consider the use of “standard” transmission strategies to facilitate the adoption of the aforementioned communication paradigm by 5G networks.

This paper shows that time varying pitch properties can be used advantageously within the segmentation step of a multi-talker diarization system. First a study is conducted to verify that changes in pitch are strong indicators of changes in the speaker. It is then highlighted that an individual’s pitch is smoothly varying and, therefore, can be predicted by means of a Kalman filter. Subsequently it is shown that if the pitch is not predictable then this is most likely due to a change in the speaker.

This work deals with efficient atom selection procedure in
a continuous dictionary, as required for instance in a Frank-
Wolfe approach within a BLASSO problem for the onedimensional
deconvolution problem. We show that efficient
maximization of a correlation between any given vector and
an atom sweeping a continuous dictionary can be performed
through a particular piece-wise linear approximation of dictionaries:
the polar approximation. We finally identify the
polar approximation as being optimal in a mean square error

Link to original article https://ieeexplore.ieee.org/document/8683546

Continuously-worn wearable sensors produce copious amounts of rich bio-behavioral time series recordings. Exploring recurring patterns, often known as motifs, in wearable time series offers critical insights into understanding the nature of human behavior. Challenges in discovering motifs from wearable recordings include noise removal, pattern generalization, and accounting for subtle variations between subsequences in one motif set.