CN-Celeb-AV: A Multi-Genre Audio-Visual Dataset for Person Recognition

Audio-visual person recognition (AVPR) has received extensive attention. However, most datasets used for AVPR research so far are collected in constrained environments, and thus cannot reflect the true performance of AVPR systems in real-world scenarios. To meet the request for research on AVPR in unconstrained conditions, this paper presents a multi-genre AVPR dataset collected `in the wild', named CN-Celeb-AV. This dataset contains more than 419k video segments from 1,136 persons from public media. In particular, we put more emphasis on two real-world complexities: (1) data in multiple genres; (2) segments with partial information. A comprehensive study was conducted to compare CN-Celeb-AV with two popular public AVPR benchmark datasets, and the results demonstrated that CN-Celeb-AV is more in line with real-world scenarios and can be regarded as a new benchmark dataset for AVPR research. The dataset also involves a development set that can be used to boost the performance of AVPR systems in real-life situations. The dataset is free for researchers and can be downloaded from http://cnceleb.org/.Comment: INTERSPEECH 202

An improved model to estimate trapping parameters in polymeric materials and its application on normal and aged low-density polyethylenes

Trapping parameters can be considered as one of the important attributes to describe polymeric materials. In the present paper, a more accurate charge dynamics model has been developed, which takes account of charge dynamics in both volts-on and off stage into simulation. By fitting with measured charge data with the highest R-square value, trapping parameters together with injection barrier of both normal and aged low-density polyethylene samples were estimated using the improved model. The results show that, after long-term ageing process, the injection barriers of both electrons and holes is lowered, overall trap depth is shallower, and trap density becomes much greater. Additionally, the changes in parameters for electrons are more sensitive than those of holes after ageing

Trapping parameters estimation of fresh and thermally-aged low-density polyethylene by using an improved trapping/detrapping model

In the present paper, trapping parameters of normal and thermally aged low-density polyethylene (LDPE) samples were estimated using the improved charge dynamic model. The results show that, after long-term thermal ageing process, the injection barrier of both electrons and holes is lowered, the overall trap depth is shallower and electron trap density becomes much greater. The latter may indicate that electrons are more sensitive to ageing than those of holes

An improved model to estimate trapping parameters in polymeric materials and its application on normal and aged low-density polyethylenes

Trapping parameters can be considered as one of the important attributes to describe polymeric materials. In the present paper, a more accurate charge dynamics model has been developed, which takes account of charge dynamics in both volts-on and off stage into simulation. By fitting with measured charge data with the highest R-square value, trapping parameters together with injection barrier of both normal and aged low-density polyethylene samples were estimated using the improved model. The results show that, after long-term ageing process, the injection barriers of both electrons and holes is lowered, overall trap depth is shallower, and trap density becomes much greater. Additionally, the changes in parameters for electrons are more sensitive than those of holes after ageing