(Downloads - 0)
For more info about our services contact : help@bestpfe.com
Table of contents
Chapter 1 Introduction
1.1 Motivation
1.1.1 Audio for robots, robots for audio
1.1.2 Audio source localization is essential
1.1.3 Conquering uncertainty
1.2 Problem
1.2.1 Problem formulation
1.2.2 General framework of source localization for robot audition
1.3 Contributions
1.4 Outline
Chapter 2 State of the art
2.1 Angle of arrival measurement
2.1.1 General concepts
2.1.1.1 Source localization cues
2.1.1.2 Far-field source
2.1.1.3 Source signal model
2.1.2 Overview of source localization methods
2.1.2.1 Generalized cross-correlation with phase transform
2.1.2.2 Time difference of arrival based methods
2.1.2.3 Steered response power based methods
2.1.2.4 Multiple signal classification based methods
2.1.3 MUSIC-GSVD algorithm
2.2 Source activity detection
2.3 Sequential filtering for a single source
2.3.1 State vector
2.3.2 Observation vector
2.3.3 Recursive Bayesian estimation
2.3.4 Nonlinear mixture Kalman filtering
2.3.5 Particle filtering
2.3.6 Occupancy grids
2.4 Sequential filtering for multiple sources
2.4.1 State vector
2.4.2 Observation vector
2.4.3 Joint probabilistic data association filter
2.4.3.1 Prediction step
2.4.3.2 Update step
2.5 Motion planning for robot audition
2.5.1 General robot motion planning
2.5.2 Motion planning for robot audition
Chapter 3 Source localization in a reverberant environment
3.1 Proposed Bayesian filtering framework
3.1.1 State vector
3.1.2 Dynamical model
3.1.2.1 Dynamical model of the robot
3.1.2.2 Dynamical model of the sound source
3.1.2.3 Full dynamical model
3.1.3 Observation vector
3.1.4 Recursive Bayesian estimation
3.2 Extended mixture Kalman filtering
3.2.1 Prediction step
3.2.2 Update step
3.2.3 Hypothesis pruning
3.2.4 Experimental evaluation
3.2.4.1 Data
3.2.4.2 Algorithm settings
3.2.4.3 Example run – Visualization
3.2.4.4 Example run – Estimated trajectories
3.2.4.5 Error rate of source location estimation
3.2.4.6 Error rate of source activity estimation
3.2.4.7 Statistical analysis
3.3 Particle filtering
3.3.1 Prediction step
3.3.2 Update step
3.3.3 Particle resampling step
3.3.4 Example run
3.4 Comparison of the extended MKF with the particle filtering
3.4.1 Data
3.4.2 Algorithm settings
3.4.3 Experimental results
3.5 Summary
Chapter 4 Multiple source localization
4.1 Learning the sensor model for multiple source localization
4.2 Proposed extended MKF with joint probabilistic data association filter
4.2.1 State and observation vectors
4.2.1.1 State vector
4.2.1.2 Observation vector
4.2.1.3 Joint associations
4.2.2 Prediction step
4.2.3 Update step
4.2.3.1 Joint association events
4.2.3.2 Update step
4.3 Experimental evaluation
4.3.1 Data
4.3.2 Algorithm settings
4.3.3 Example run
4.3.4 Statistical result
4.4 Summary
Chapter 5 Optimal motion control for robot audition
5.1 Cost function
5.1.1 Shannon entropy criterion
5.1.2 Standard deviation criterion
5.2 Monte Carlo tree search
5.2.1 Algorithm outline
5.2.2 Optimism in the face of uncertainty
5.3 Adapting MCTS for robot audition
5.3.1 Formulation
5.3.2 Selection
5.3.2.1 Bounded entropy
5.3.2.2 Bounded standard deviation
5.3.3 Expansion
5.3.4 Simulation
5.3.5 Backpropagation
5.4 Evaluation
5.4.1 Experimental protocol
5.4.2 Example trajectory
5.4.3 MCTS vs other motion planning approaches
5.4.3.1 Entropy criterion
5.4.3.2 Standard deviation criterion
5.4.4 Relation of both criteria with estimation error
5.4.5 Effect of the discount factor
5.4.5.1 Entropy criterion
5.4.5.2 Standard deviation criterion
5.5 Summary
Chapter 6 Conclusion and perspectives
6.1 Conclusion
6.2 Perspectives
Appendix A
Résumé en français
A.1 Introduction
A.2 État de l’art
A.3 Localisation d’une source en environnement réverbérant
A.4 Localisation de plusieurs sources
A.5 Planification de mouvement pour l’audition
A.6 Conclusion et perspectives
Bibliography
