Title:

Using knowledge to organize sound: The prediction-driven approach to computational auditory scene analysis and its application to speech/nonspeech mixtures

Author(s):

Ellis, Daniel P. W.

Date:

1999

Type:

Articles

Department:

Electrical Engineering

Volume:

27

Permanent URL:

http://hdl.handle.net/10022/AC:P:12554

Book/Journal Title:

Speech Communication

Abstract:

Computational auditory scene analysis — modeling the human ability to organize sound mixtures according to their sources — has experienced a rapid evolution from simple implementations of psychoacoustically inspired rules to complex systems able to process demanding real-world sounds. Phenomena such as the continuity illusion and phonemic restoration show that the brain is able to use a wide range of knowledge-based contextual constraints when interpreting obscured or overlapping mixtures: To model such processing, we need architectures that operate by confirming hypotheses about the observations rather than relying on directly extracted descriptions. One such architecture, the 'prediction-driven' approach, is presented along with results from its initial implementation. This architecture can be extended to take advantage of the high-level knowledge implicit in today's speech recognizers by modifying a recognizer to act as one of the 'component models' providing the explanations of the signal mixture. A preliminary investigation indicates the viability of this approach while at the same time raising a number of issues which are discussed. These results point to the conclusion that successful scene analysis must, at every level, exploit abstract knowledge about sound sources.

Subject(s):

Artificial intelligence
Music

DOI:

http://dx.doi.org/10.1016/S0167-6393(98)00083-1

Item views:

30

Metadata:

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