Improving Blind Source Separation Performance By Adaptive Array Geometries For Humanoid Robots
This work addresses signal separation challenges in robotics, but it appears incremental as it builds on existing methods with a new adaptation approach.
The paper tackles the problem of improving blind source separation for humanoid robots by proposing an adaptation algorithm that adjusts microphone array geometry, resulting in confirmed efficacy in simulated experiments.
In this paper, the concept of an adaptation algorithm is proposed, which can be used to blindly adapt the microphone array geometry of a humanoid robot such that the performance of the underlying signal separation algorithm is improved. As a decisive feature, an online performance measure for blind source separation is introduced which allows a robust and reliable estimation of the instantaneous separation performance based on currently observable data. Experimental results from a simulated environment confirm the efficacy of the concept.