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Audio signals, such as speech and ambient sounds convey rich information pertaining to a user’s activity, mood or intent. Enabling machines to understand this contextual information is necessary to bridge

Audio signals, such as speech and ambient sounds convey rich information pertaining to a user’s activity, mood or intent. Enabling machines to understand this contextual information is necessary to bridge the gap in human-machine interaction. This is challenging due to its subjective nature, hence, requiring sophisticated techniques. This dissertation presents a set of computational methods, that generalize well across different conditions, for speech-based applications involving emotion recognition and keyword detection, and ambient sounds-based applications such as lifelogging.

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    Date Created
    • 2015
    Resource Type
  • Text
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    Note
    • Partial requirement for: Ph.D., Arizona State University, 2015
      Note type
      thesis
    • Includes bibliographical references (p. 134-144)
      Note type
      bibliography
    • Field of study: Electrical engineering

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    by Mohit Shah

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