Automatic Modeling of Acoustic Perception of Breathiness in Pathological Voices

2009 ◽  
Vol 56 (4) ◽  
pp. 932-940 ◽  
Author(s):  
Adel Ruiz
Keyword(s):  
Automatic Modeling ◽  
Acoustic Perception

scholarly journals A reconfiguration strategy for modular robots using origami folding

2018 ◽  
Vol 38 (1) ◽  
pp. 73-89 ◽  
Author(s):  
Meibao Yao ◽  
Christoph H. Belke ◽  
Hutao Cui ◽  
Jamie Paik
Keyword(s):  
Heuristic Algorithm ◽  
Minimum Energy ◽  
Computational Time ◽  
Modular Robots ◽  
Automatic Modeling ◽  
Minimum Energy Consumption ◽  
Modular Units ◽  
Optimal Reconfiguration ◽  
Algorithmic Framework ◽  
Modeling Algorithm

Reconfigurability in versatile systems of modular robots is achieved by changing the morphology of the overall structure as well as by connecting and disconnecting modules. Recurrent connectivity changes can cause misalignment that leads to mechanical failure of the system. This paper presents a new approach to reconfiguration, inspired by the art of origami, that eliminates connectivity changes during transformation. Our method consists of an energy-optimal reconfiguration planner that generates an initial 2D assembly pattern and an actuation sequence of the modular units, both resulting in minimum energy consumption. The algorithmic framework includes two approaches, an automatic modeling algorithm as well as a heuristic algorithm. We further demonstrate the effectiveness of our method by applying the algorithms to Mori, a modular origami robot, in simulation. Our results show that the heuristic algorithm yields reconfiguration schemes with high quality, compared with the automatic modeling algorithm, simultaneously saving a considerable amount of computational time and effort.

Efficient acoustic perception for virtual AI agents

2021 ◽  
Vol 4 (3) ◽  
pp. 1-13
Author(s):  
Mike Chemistruck ◽  
Andrew Allen ◽  
John Snyder ◽  
Nikunj Raghuvanshi
Keyword(s):  
Source Code ◽  
Auditory Masking ◽  
Acoustic Perception ◽  
Sound Event ◽  
Complex Scenes

We model acoustic perception in AI agents efficiently within complex scenes with many sound events. The key idea is to employ perceptual parameters that capture how each sound event propagates through the scene to the agent's location. This naturally conforms virtual perception to human. We propose a simplified auditory masking model that limits localization capability in the presence of distracting sounds. We show that anisotropic reflections as well as the initial sound serve as useful localization cues. Our system is simple, fast, and modular and obtains natural results in our tests, letting agents navigate through passageways and portals by sound alone, and anticipate or track occluded but audible targets. Source code is provided.

Sign in / Sign up

Export Citation Format

Share Document