Trends in Machine Learning for Signal Processing

Tülay Adali, Konstantinos Diamantaras, Jan Larsen

AbstractBy putting the accent on "learning" from the data and the environment, the MLSP TC provides the essential bridge between the machine learning and signal processing communities. MLSP techniques have always been attractive solutions for traditional signal processing applications such as pattern recognition, speech, audio, and video processing. More importantly, owing to their polyvalent nature, these methods are also primary candidates for a new wave of emerging applications such as brain-computer interface, multimodal data fusion and processing, behavior and emotion recognition, and learning in environments such as social networks. At this session, we will discuss the role MLSP plays in such emerging applications as well as major paradigm shifts in learning as demonstrated by cognitive systems. We shall also explore what these paradigm shifts offer for the signal processing community.
Keywordstrends, machine learning, signal processing, icassp 2011
TypeMisc [Presentation]
Journal/Book/ConferenceICASSP 2011
Year2011    Month May
PublisherIEEE Press
NoteCompanion paper at
Publication linkhttp://
BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing

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