6,408 research outputs found
Uncertainty Estimation in Deep Speech Enhancement Using Complex Gaussian Mixture Models
Single-channel deep speech enhancement approaches often estimate a single
multiplicative mask to extract clean speech without a measure of its accuracy.
Instead, in this work, we propose to quantify the uncertainty associated with
clean speech estimates in neural network-based speech enhancement. Predictive
uncertainty is typically categorized into aleatoric uncertainty and epistemic
uncertainty. The former accounts for the inherent uncertainty in data and the
latter corresponds to the model uncertainty. Aiming for robust clean speech
estimation and efficient predictive uncertainty quantification, we propose to
integrate statistical complex Gaussian mixture models (CGMMs) into a deep
speech enhancement framework. More specifically, we model the dependency
between input and output stochastically by means of a conditional probability
density and train a neural network to map the noisy input to the full posterior
distribution of clean speech, modeled as a mixture of multiple complex Gaussian
components. Experimental results on different datasets show that the proposed
algorithm effectively captures predictive uncertainty and that combining
powerful statistical models and deep learning also delivers a superior speech
enhancement performance.Comment: 5 pages, 4 figure
Uncertainty-Aware Imitation Learning using Kernelized Movement Primitives
During the past few years, probabilistic approaches to imitation learning
have earned a relevant place in the literature. One of their most prominent
features, in addition to extracting a mean trajectory from task demonstrations,
is that they provide a variance estimation. The intuitive meaning of this
variance, however, changes across different techniques, indicating either
variability or uncertainty. In this paper we leverage kernelized movement
primitives (KMP) to provide a new perspective on imitation learning by
predicting variability, correlations and uncertainty about robot actions. This
rich set of information is used in combination with optimal controller fusion
to learn actions from data, with two main advantages: i) robots become safe
when uncertain about their actions and ii) they are able to leverage partial
demonstrations, given as elementary sub-tasks, to optimally perform a higher
level, more complex task. We showcase our approach in a painting task, where a
human user and a KUKA robot collaborate to paint a wooden board. The task is
divided into two sub-tasks and we show that using our approach the robot
becomes compliant (hence safe) outside the training regions and executes the
two sub-tasks with optimal gains.Comment: Submitted to IROS1
SOTIF Entropy: Online SOTIF Risk Quantification and Mitigation for Autonomous Driving
Autonomous driving confronts great challenges in complex traffic scenarios,
where the risk of Safety of the Intended Functionality (SOTIF) can be triggered
by the dynamic operational environment and system insufficiencies. The SOTIF
risk is reflected not only intuitively in the collision risk with objects
outside the autonomous vehicles (AVs), but also inherently in the performance
limitation risk of the implemented algorithms themselves. How to minimize the
SOTIF risk for autonomous driving is currently a critical, difficult, and
unresolved issue. Therefore, this paper proposes the "Self-Surveillance and
Self-Adaption System" as a systematic approach to online minimize the SOTIF
risk, which aims to provide a systematic solution for monitoring,
quantification, and mitigation of inherent and external risks. The core of this
system is the risk monitoring of the implemented artificial intelligence
algorithms within the AV. As a demonstration of the Self-Surveillance and
Self-Adaption System, the risk monitoring of the perception algorithm, i.e.,
YOLOv5 is highlighted. Moreover, the inherent perception algorithm risk and
external collision risk are jointly quantified via SOTIF entropy, which is then
propagated downstream to the decision-making module and mitigated. Finally,
several challenging scenarios are demonstrated, and the Hardware-in-the-Loop
experiments are conducted to verify the efficiency and effectiveness of the
system. The results demonstrate that the Self-Surveillance and Self-Adaption
System enables dependable online monitoring, quantification, and mitigation of
SOTIF risk in real-time critical traffic environments.Comment: 16 pages, 10 figures, 2 tables, submitted to IEEE TIT
- …