Intelligent Analysis of Composite Acoustic Signals          (Computer and Information Science)
{'Code': '05020000', 'Directorate': {'Abbreviation': 'CSE', 'LongName': 'Direct For Computer & Info Scie & Enginr'}, 'Division': {'Abbreviation': 'IIS', 'LongName': 'Div Of Information & Intelligent Systems'}}
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This research is concerned with machine perception and                  analysis                                                                        of complex sound signals.  The goaL is to reliably identify and                 track simultaneous acoustic sources in a monaural signal.  In the                                                                                               proposed architecture, perception results from the interaction of                                                                                               data-driven and expectation-driven agents.  The allocation of                   resources to systems agents, and the control of feedback loops                  between different levels of interpretation of the time-varying                  signal are approached with strategies that simulate real-time                   problem solving.  At the lowest level of analysis, multi-rate                   signal processing, used in conjection with focus-switching                      heuristics, ought to yield high resolution simultaneously in time                                                                                               and frequency, thus giving an efficient method for improving upon                                                                                               traditional bandwidth-time tradeoffs.  Source coherence criteria                                                                                                derived from psychoacoustic work on auditory streaming (including                                                                                               correlated AM and FM modulation among partials) are expected to                 be useful for separating sources when more familiar methods do                  not suffice.                                                                            The proposed system relies on a learning co-processor to                attune itself to increasingly elusive aspects of a signal.                      Relevant techniques include traditional parameter adaptation,                   numerical taxonomy, syntatic pattern matching and concept                       learning. Especially important is the development of hybrid                     methods that combine parametric and structural views.  In                       summary, the research addresses key areas of acoustic analysis as                                                                                               well as broader issues in the architecture of artificial                        intelligence.
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{'Name': 'Stanford University', 'CityName': 'STANFORD', 'ZipCode': '943052004', 'PhoneNumber': '6507232300', 'StreetAddress': '450 JANE STANFORD WAY', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'California', 'StateCode': 'CA', 'CONGRESSDISTRICT': '16', 'CONGRESS_DISTRICT_ORG': 'CA16', 'ORG_UEI_NUM': 'HJD6G4D6TJY5', 'ORG_LGL_BUS_NAME': 'THE LELAND STANFORD JUNIOR UNIVERSITY', 'ORG_PRNT_UEI_NUM': None}
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{'Code': '6840', 'Text': 'ROBOTICS'}
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