U.C. Berkeley EECS225d Spring 1995
Audio Signal Processing in Humans and Machines

Meeting: MWF 2-3, 247 Cory

Course overview/outline
(postscript version)
A Note for Netscape Users
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Midterm I Questions, w/o Answers (uncompressed PS)
Midterm I Questions, with Answers (uncompressed PS)
Midterm II Questions, w/o Answers (uncompressed PS)
Midterm II Questions, with Answers (uncompressed PS)

Lecture Notes

  • Lecture 2, 1/20: Early History of Speech and Music Synthesis
  • Lecture 3, 1/23: Speech Analysis/Synthesis Overview
  • Lecture 4, 1/25: Speech Production Models I
  • Lecture 5, 1/27: Early History of Speech Recognition
  • Lecture 6, 1/30: Speech Recognition Overview
  • Lecture 7, 2/1: Speech Production Models II
  • Lecture 8, 2/3: Phonemes and features (Ohala)
  • Lecture 9, 2/6: Music Production Models I
  • Lecture 10, 2/8: Music Production Models II
  • Lecture 11, 2/10: Room Acoustics
  • Lecture 12, 2/13: Physiology of the Ear
  • Lecture 13, 2/15: Engineering Models of the Cochlea (Lazzaro)
  • Lecture 14, 2/17: Psychophysics and Modelling of Speech Perception I (Greenberg)
  • Lecture 15, 2/22: Engineering Models of Human Formant Tracking (Holton)
  • --Supplemental paper from Tom Holton
  • Lecture 16, 2/24: Psychophysics and Modelling of Speech Perception II (Greenberg)
  • Lecture 17, 2/27: Engineering models of psychophysics (Hermansky)
  • Lecture 18, 3/1: Pitch Perception
  • Lecture 19, 3/3: Musical Pitch
  • (Lecture 20, 3/6: Midterm I)
  • Lecture 21, 3/8: Pitch Detection of Speech and Music
  • Lecture 22, 3/10: How Do Humans Process and Recognize Speech? (Allen)
  • Lecture 23, 3/13: Vocoders I
  • Lecture 24, 3/15: Vocoders II
  • Lecture 25, 3/17: Vocoders III
  • Lecture 26, 3/20 Audio examples from Klatt paper
  • Lecture 27, 3/22: Music Synthesis
  • Lecture 28, 3/24: GloveTalk II (Sid Fells)
  • (spring break: 3/27-3/31)
  • Lecture 29, 4/3: Intro to Pattern Recognition I
  • Lecture 30, 4/5: Intro to Pattern Recognition II
  • Lecture 31, 4/7: Intro to statistical pattern recognition
  • Lecture 32, 4/10: Template matching
  • Lecture 33, 4/12: Hidden Markov Models
  • Lecture 34, 4/14: Hybrid HMM/ANN Systems
  • Lecture 35, 4/17: The Berkeley Restaurant Project (Jurafsky)
  • Lecture 36, 4/19: Text-To-Speech Systems (O'Malley)
  • Lecture 37, 4/21: The Census Task (Cole)
  • --Note: the next two sets of slides are printable but not viewable in ghostview, sorry for the inconvienence.
  • --Ron Cole's Alphabet Recognition Slides
  • --Ron Cole's Census Task Slides
  • (Lecture 38, 4/24: Midterm II)
  • Lecture 39, 4/26: Using Perceptual models for speech recognition (Ghitza)
  • Lecture 40, 4/28: Using Perceptual models for Analysis/Synthesis (Ghitza)

    • LaTeX Source Files

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    Maintained by:
    J. Eric Fosler
    fosler@ICSI.Berkeley.EDU
    $Date: 1997/01/21 01:51:58 $