Using Deep Neural Networks for Smoothing Pitch Profiles in Connected Speech

This paper presents a new pitch tracking smoother based on deep neural networks (DNN). It leverages Long Short-Term Memories, a particular kind of recurrent neural network, for correcting pitch detection errors produced by state-of-the-art Pitch Detection Algorithms. The proposed system has been extensively tested using two reference benchmarks for English and exhibited very good performances in correcting pitch detection algorithms outputs when compared with the gold standard obtained with laryngographs

A follow-me algorithm for AR.Drone using MobileNet-SSD and PID control

Treballs Finals de Grau d'Enginyeria Informàtica, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2018, Director: Lluís Garrido Ostermann[en] In recent years the industry of quadcopters has experimented a boost. The appearance of inexpensive drones has led to the growth of the recreational use of this vehicles, which opens the door to the creation of new applications and technologies. This thesis presents a vision-based autonomous control system for an AR.Drone 2.0. A tracking algorithm is developed using onboard vision systems without relying on additional external inputs. In particular, the tracking algorithm is the combination of a trained MobileNet-SSD object detector and a KCF tracker. The noise induced by the tracker is decreased with a Kalman filter. Furthermore, PID controllers are implemented for the motion control of the quadcopter, which process the output of the tracking algorithm to move the drone to the desired position. The final implementation was tested indoors and the system yields acceptable results

Using Actor-Critic Reinforcement Learning for Control of a Quadrotor Dynamics

This paper presents a quadrotor controller using reinforcement learning to generate near-optimal control signals. Two actor-critic algorithms are trained to control quadrotor dynamics. The dynamics are further simplified using small angle approximation. The actor-critic algorithm’s control policy is derived from Bellman’s equation providing a sufficient condition to optimality. Additionally, a smoother converter is implemented into the trajectory providing more reliable results. This paper provides derivations to the quadrotor’s dynamics and explains the control using the actor-critic algorithm. The results and simulations are compared to solutions from a commercial, optimal control solver, called DIDO