環境変動を考慮したロボット聴覚のための音源分離

抄録

This paper describes a novel sound source separation method for a robot that needs to cope with dynamically changing noises in the real world. A sound source separation method, Geometric Source Separation (GSS), is promising because it has high separation performance but does not require a high computational cost. However, GSS has several issues when applied to real-world applications such as robot audition systems that are used in dynamically changing environments. To improve performance in dynamically changing environments, we propose two effective techniques. One is Adaptive Step-size control (AS) this adaptively sets the step-size to the optimum value. The other is Optima Controlled Recursive Average that improves the precision of an estimated separation matrix, and thus achieves high separation performance. We evaluated GSS with and without our proposed methods using an 8ch microphone array embedded in Honda ASIMO. Experimental results showed that the proposed methods improved GSS performance in dynamically changing environment.