A NOVEL EGO-NOISE SUPPRESSION ALGORITHM FOR ACOUSTIC SIGNAL ENHANCEMENT IN AUTONOMOUS SYSTEMS

The use of autonomous systems (ASs), such as humanoid robots, drones or self-driving vehicles, has expanded significantly in recent years. For such systems, acoustic scene analysis can provide useful information about the environment and supports the AS to react appropriately. However, compared to most other application areas, analysis and enhancement of acoustic signals captured by ASs is not only complicated by external sources of signal degradation but also by very specific challenges like internal and self-created ego-noise. This paper first gives an overview of a typical acoustic scenario an AS is exposed to. Then, we consider the specific problem of ego-noise suppression and propose to use motor data to predict the characteristic time-varying harmonic structure of ego-noise. This knowledge is then incorporated into a multichannel dictionary-based algorithm. The resulting two-stage ego-noise reduction scheme is evaluated for ego-noise of a humanoid robot and outperforms a comparable method that uses no motor data but a a larger dictionary.