Abstract
Autonomous systems (ASs) operating in real-world environments are exposed to a plurality and diversity of sounds that carry a wealth of information for perception in cognitive dynamic systems. While the importance of the acoustic modality for humans as “ASs” is obvious, it is investigated to what extent current technical ASs operating in scenarios filled with airborne sound exploit their potential for supporting self-awareness. As a first step, the state of the art of relevant generic techniques for acoustic scene analysis (ASA) is reviewed, i.e., source localization and the various facets of signal enhancement, including spatial filtering, source separation, noise suppression, dereverberation, and echo cancellation. Then, a comprehensive overview of current techniques for ego-noise suppression, as a specific additional challenge for ASs, is presented. Not only generic methods for robust source localization and signal extraction but also specific models and estimation methods for ego-noise based on various learning techniques are discussed. Finally, active sensing is considered with its unique potential for ASA and, thus, for supporting self-awareness of ASs. Therefore, recent techniques for binaural listening exploiting head motion, for active localization and exploration, and for active signal enhancement are presented, with humanoid robots as typical platforms. Underlining the multimodal nature of self-awareness, links to other modalities and nonacoustic reference information are pointed out where appropriate.
Highlights
Recent decades spawned striking examples of what is commonly referred to as autonomous systems (ASs), such as self-driving cars, robots operating in our daily environment or exploring unknown worlds in deep sea or outer space, unmanned aerial vehicles (UAVs) for logistics, air surveillance and combat, autonomous weapon systems, and many more
While the importance of the acoustic modality for humans as “ASs” is obvious, relatively little attention was paid, so far to the acoustic modality of airborne sound1 for ASs, and especially the specific challenges of ASs with its self-created noise resulting, e.g., from its own movements. This can be attributed to the fact that acoustic human–machine communication and acoustic scene analysis (ASA) only recently reached a stage of maturity which allows operation outside highly constrained acoustic environments, and the complexity of the acoustic scenario faced by ASs is often viewed as
We distinguish between ASA and computational auditory scene analysis (CASA) [9], [10]: ASA does not refer to the functionality of the human auditory system
Summary
Recent decades spawned striking examples of what is commonly referred to as autonomous systems (ASs), such as self-driving cars, robots operating in our daily environment or exploring unknown worlds in deep sea or outer space, unmanned aerial vehicles (UAVs) for logistics, air surveillance and combat, autonomous weapon systems, and many more. While the importance of the acoustic modality for humans as “ASs” is obvious, relatively little attention was paid, so far to the acoustic modality of airborne sound for ASs, and especially the specific challenges of ASs with its self-created noise resulting, e.g., from its own movements This can be attributed to the fact that acoustic human–machine communication and acoustic scene analysis (ASA) only recently reached a stage of maturity which allows operation outside highly constrained acoustic environments, and the complexity of the acoustic scenario faced by ASs is often viewed as. The sensor unit enables the AS to perceive on the one hand its own contribution to the acoustic scene, e.g., by emitting ego-noise, and on the other hand its ambient environment by, e.g., localizing surrounding acoustic sources and extracting their signals Both tasks are crucial for an AS to achieve acoustic self-awareness. Computationally expensive algorithms are implemented and processed offline on external hardware
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