Model-Based Scene Analysis

Daniel P. W. Ellis

Model-Based Scene Analysis
Ellis, Daniel P. W.
Chapters (layout features)
Electrical Engineering
Persistent URL:
Book/Journal Title:
Computational Auditory Scene Analysis: Principles, Algorithms, and Applications
Book Author:
Wang, DeLiang
Brown, Guy J.
Publisher Location:
Hoboken, N.J.
When multiple sound sources are mixed together into a single channel (or a small number of channels) it is in general impossible to recover the exact waveforms that were mixed; indeed, without some kind of constraints on the form of the component signals, it is impossible to separate them at all. These constraints could take several forms. For instance, given a particular family of processing algorithms (such as linear filtering, or selection of individual time-frequency cells in a spectrogram), constraints could be defined in terms of the relationships between the set of resulting output signals, such as statistical independence [3, 41], or clustering of a variety of properties that indicate distinct sources [1, 45]. These approaches are concerned with the relationships between the properties of the complete set of output signals, rather than the specific properties of any individual output; in general, the individual sources could take any form. Another way to express the constraints is to specify the form that the individual sources can take, regardless of the rest of the signal. These restrictions may be viewed as "prior models" for the sources, and source separation then becomes the problem of finding a set of signals that combine together to give the observed mixture signal at the same time as conforming in some optimal sense to the prior models. This is the approach to be examined in this chapter.
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Suggested Citation:
Daniel P. W. Ellis, , Model-Based Scene Analysis, Columbia University Academic Commons, .

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