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Abstract-Musical beat tracking is one of the effective technologies for human-robot interaction such as musical sessions. Since such interaction should be performed in various environments in a natural way, musical beat tracking for a robot should cope with noise sources such as environmental noise, its own motor noises, and self voices, by using its own microphone. This paper addresses a musical beat tracking robot which can step, scat and sing according to musical beats by using its own microphone. To realize such a robot, we propose a robust beat tracking method by introducing two key techniques, that is, spectro-temporal pattern matching and echo cancellation. The former realizes robust tempo estimation with a shorter window length, thus, it can quickly adapt to tempo changes. The latter is effective to cancel self noises such as stepping, scatting, and singing. We implemented the proposed beat tracking method for Honda ASIMO. Experimental results showed ten times faster adaptation to tempo changes and high robustness in beat tracking for stepping, scatting and singing noises. We also demonstrated the robot times its steps while scatting or singing to musical beats.

As robotic technology plays an increasing role in human lives, "robot audition", human-robot communication, is of great interest, and robot audition needs to be robust and adaptable for dynamic environments. This paper addresses sound source localization working in dynamic environments for robots. Previously, noise robustness and dynamic localized sound selection have been enormous issues for practical use. To correct the issues, a new localization system "Selective Attention System" is proposed. The system has four new functions: localization with Generalized EigenValue Decomposition of correlation matrices for noise robustness("Localization with GEVD"), sound source cancellation and focus ("Target Source Selection"), human-like dynamic Focus of Attention ("Dynamic FoA"), and correlation matrix estimation for robotic head rotation ("Correlation Matrix Estimation"). All are achieved by the dynamic design of correlation matrices. The system is implemented into a humanoid robot, and the experimental validation is successfully verified even when the robot microphones move dynamically.

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