Blackboard Systems for Cognitive Audition

Abstract

An essential part of auditory scene understanding is building an internal model of the world surrounding the listener. This internal representation can be mimicked computationally via a blackboard-system-based software architecture. Blackboard systems allow efficient integration of different perceptual modalities, algorithms, and data representations into a coherent and flexible computational framework. The term “blackboard” in this context stands for a flexible and compositional internal data representation, allowing individual software modules to access and process available information. This modular architecture also makes the system adaptable to different application scenarios and provides interfaces to incorporate feedback paths, which allows the system to derive task-optimal active behavior from the internal model. Extending conventional blackboard systems with modern machine-learning techniques, specifically probabilistic modeling and neural networks, enables the system to incorporate learning strategies into this computational framework. Additionally, online learning and adaptation strategies can be integrated into the data representation within the blackboard. This is particularly useful for developing feedback approaches. This chapter gives a review of existing blackboard systems for different applications and provides the necessary theoretical foundations. Subsequently, novel extensions that were recently introduced in the context of binaural scene analysis and understanding are presented and discussed. A special focus is set on possibilities for incorporating feedback and learning strategies into the framework.