Name
Abstract | ||
|---|---|---|
Acoustic tracking of a moving sound source is relevant in many domains including robotic phonotaxis and human-robot interaction. Typical approaches rely on processing time-difference-of-arrival cues obtained via multi-microphone arrays with Kalman or particle filters, or other computationally expensive algorithms. We present a novel bio-inspired solution to acoustic tracking that uses only two microphones. The system is based on a neural mechanism coupled with a model of the peripheral auditory system of lizards. The peripheral auditory model provides sound direction information which the neural mechanism uses to learn the target's velocity via fast correlation-based unsupervised learning. Simulation results for tracking a pure tone acoustic target moving along a semi-circular trajectory validate our approach. Three different angular velocities in three separate trials were employed for the validation. A comparison with a Braitenberg vehicle-like steering strategy shows the improved performance of our learning-based approach. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-43488-9_8 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Binaural acoustic tracking,Correlation learning,Lizard peripheral auditory system | Computer vision,Computer science,Particle filter,Pure tone,Auditory system,Kalman filter,Unsupervised learning,Artificial intelligence,Binaural recording,Trajectory,Acoustic tracking | Conference |
Volume | ISSN | Citations |
9825 | 0302-9743 | 1 |
PageRank | References | Authors |
0.35 | 9 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Danish Shaikh | 1 | 28 | 4.87 |
| Poramate Manoonpong | 2 | 94 | 11.02 |