Amitav Das,

Microsoft Research


Usually signal processing researchers are happy with their various ways of slicing and dicing the signals to explore various aspects of the signals, while the pattern recognition people are busy looking at various recognition/classification algorithms using whatever “features” from the signal are “given” to them. Usually these two groups of researchers each go their own way. But, for a lot of applications it is important to consider both the feature selection and classification method together which is typically NOT done. For example, MFCC is used in speech recognition as a feature which is supposed to be “speaker-independent” and represent what you are saying. But the same feature is used by people working in speaker identification as well!

In my talk, I will give a brief overview of popular and emerging signal processing applications and then pick one of my research areas, namely user-identification, and show how judicious feature selection helps to keep the classification part simple and allows one to develop systems which provide high performance at very low complexity.


Date: 2008-Oct-21     Time: 10:30:00     Room: 336

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