The goal of the Two!Ears project is to develop an intelligent, active computational model of auditory perception and experience in a multi-modal context. In order to do so, the system must be able to recognise acoustic sources and optical objects, and achieve perceptual organisation of sound in the same manner as human listeners do. Bregman has referred to the latter phenomenon as ASA [Bregman1990], and to reproduce this ability in a machine system a number of factors must be considered:

  • ASA involves diverse sources of knowledge, including both primitive (innate) grouping heuristics and schema-driven (learned) grouping principles.
  • Solving the ASA problem requires the close interaction of top-down and bottom-up processes through feedback loops.
  • Auditory processing is flexible, adaptive, opportunistic and context-dependent.

The characteristics of ASA are well-matched to those of blackboard problem-solving architectures. A blackboard system consists of a group of independent experts (knowledge sources) that communicate by reading and writing data on a globally-accessible data structure (blackboard). The blackboard is typically divided into layers, corresponding to data, hypotheses and partial solutions at different levels of abstraction. Given the contents of the blackboard, each knowledge source indicates the actions that it would like to perform; these actions are then coordinated by a scheduler, which determines the order in which actions will be carried out.

Blackboard systems were introduced by [Erman1980] as an architecture for speech understanding, in their Hearsay-II system. In the 1990s, a number of authors described blackboard-based systems for machine hearing [Cooke1993], [Lesser1995], [Ellis1996], [Godsmark1999]. All of these systems were in most respects conventional blackboard architectures, in which the knowledge sources employed rule-based heuristics. In contrast, the Two!Ears architecture aims to exploit recent developments in machine learning, by combining the flexibility of a blackboard architecture with powerful learning algorithms afforded by probabilistic graphical models.


Fig. 42 Overview of the blackboard architecture of Two!Ears.

The general structure of the Blackboard system is shown in Fig. 42. It consists of different knowledge sources that can put data on and receive data from the blackboard. In addition, special knowledge sources can perceive data from outside (ear signals) or send data to the outside (turn the head). The management of the different processes going on in the blackboard is achieved by monitoring and scheduling which is performed by two independent modules.

Read on for further details on the blackboard architecture, details on the knowledge sources, or start with use the blackboard system.

[Bregman1990]Bregman, A. S. (1990), Auditory scene analysis: The perceptual organization of sound, The MIT Press, Cambridge, MA, USA.
[Erman1980]Erman, L. D., Hayes-Roth, F., Lesser, V. R., and Reddy, D. R. (1980), “The Hearsay-II speech understanding system: integrating knowledge to resolve uncertainty,” Computing Surveys 12(2), pp. 213–253.
[Cooke1993]Cooke, M., Brown, G. J., Crawford, M., and Green, P. (1993), “Computational auditory scene analysis: listening to several things at once,” Endeavour 17(4), pp. 186–190.
[Lesser1995]Lesser, V. R., Nawab, S. H., and Klassner, F. I. (1995), “IPUS: An architecture for the integrated processing and understanding of signals,” Artificial Intelligence 77, pp. 129–171.
[Ellis1996]Ellis, D. P. W. (1996), “Prediction-driven computational auditory scene analysis,” PhD thesis, Massachusetts Institute of Technology.
[Godsmark1999]Godsmark, D. and Brown, G. J. (1999), “A Blackboard Architecture for Computational Auditory Scene Analysis,” Speech Commun. 27(3-4), pp. 351–366, URL