The evolution of compression and enhancement algorithms necessitates an
accurate quality assessment for point clouds. Previous works consistently
regard point cloud quality assessment (PCQA) as a MOS regression problem and
devise a deterministic mapping, ignoring the stochasticity in generating MOS
from subjective tests. Besides, the viewpoint switching of 3D point clouds in
subjective tests reinforces the judging stochasticity of different subjects
compared with traditional images. This work presents the first probabilistic
architecture for no-reference PCQA, motivated by the labeling process of
existing datasets. The proposed method can model the quality judging
stochasticity of subjects through a tailored conditional variational
autoencoder (CVAE) and produces multiple intermediate quality ratings. These
intermediate ratings simulate the judgments from different subjects and are
then integrated into an accurate quality prediction, mimicking the generation
process of a ground truth MOS. Specifically, our method incorporates a Prior
Module, a Posterior Module, and a Quality Rating Generator, where the former
two modules are introduced to model the judging stochasticity in subjective
tests, while the latter is developed to generate diverse quality ratings.
Extensive experiments indicate that our approach outperforms previous
cutting-edge methods by a large margin and exhibits gratifying cross-dataset
robustness.

通过使用概率建模，本文提出了一种新的点云质量评估方法，使用条件变分自编码器对主观测试的评判随机性进行建模，并生成多个中间质量评分，最终将这些评分整合为准确的质量预测，实现对点云质量的准确评估。实验证明，该方法在不同数据集上表现出色，超越了先前领先的方法。

基于不确定性的无参考点云质量评估

Uncertainty-aware No-Reference Point Cloud Quality Assessment

The INTERSPEECH 2020 Deep Noise Suppression (DNS) Challenge is intended to
promote collaborative research in real-time single-channel Speech Enhancement
aimed to maximize the subjective (perceptual) quality of the enhanced speech. A
typical approach to evaluate the noise suppression methods is to use objective
metrics on the test set obtained by splitting the original dataset. While the
performance is good on the synthetic test set, often the model performance
degrades significantly on real recordings. Also, most of the conventional
objective metrics do not correlate well with subjective tests and lab
subjective tests are not scalable for a large test set. In this challenge, we
open-sourced a large clean speech and noise corpus for training the noise
suppression models and a representative test set to real-world scenarios
consisting of both synthetic and real recordings. We also open-sourced an
online subjective test framework based on ITU-T P.808 for researchers to
reliably test their developments. We evaluated the results using P.808 on a
blind test set. The results and the key learnings from the challenge are
discussed. The datasets and scripts can be found here for quick access
this https URL

介绍了交互式语音技术协会 2020 年举办的深度噪声抑制挑战赛，通过提供大规模干净语音和噪声语料库对话音频，以及基于 ITU-T P.808 的在线主观测试框架，比较了不同算法的噪声抑制效果。