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Music and Probability

Cover Image Copyright Year: 2007
Author(s): David Temperley
Publisher: MIT Press
Content Type : Books & eBooks
Topics: General Topics for Engineers
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Abstract

In Music and Probability, David Temperley explores issues in music perception and cognition from a probabilistic perspective. The application of probabilistic ideas to music has been pursued only sporadically over the past four decades, but the time is ripe, Temperley argues, for a reconsideration of how probabilities shape music perception and even music itself. Recent advances in the application of probability theory to other domains of cognitive modeling, coupled with new evidence and theoretical insights about the working of the musical mind, have laid the groundwork for more fruitful investigations. Temperley proposes computational models for two basic cognitive processes, the perception of key and the perception of meter, using techniques of Bayesian probabilistic modeling. Drawing on his own research and surveying recent work by others, Temperley explores a range of further issues in music and probability, including transcription, phrase perception, pattern perception, harmony, improvisation, and musical styles.Music and Probability--the first full-length book to explore the application of probabilistic techniques to musical issues--includes a concise survey of probability theory, with simple examples and a discussion of its application in other domains. Temperley relies most heavily on a Bayesian approach, which not only allows him to model the perception of meter and tonality but also sheds light on such perceptual processes as error detection, expectation, and pitch identification. Bayesian techniques also provide insights into such subtle and advanced issues as musical ambiguity, tension, and "grammaticality," and lead to interesting and novel predictions about compositional practice and differences between musical styles.

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      Front Matter

      Page(s): i - xi
      Copyright Year: 2007

      MIT Press eBook Chapters

      This chapter contains sections titled: Half Title, Title, Copyright, Dedication, Contents, Preface View full abstract»

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      Introduction

      Page(s): 1 - 6
      Copyright Year: 2007

      MIT Press eBook Chapters

      In Music and Probability, David Temperley explores issues in music perception and cognition from a probabilistic perspective. The application of probabilistic ideas to music has been pursued only sporadically over the past four decades, but the time is ripe, Temperley argues, for a reconsideration of how probabilities shape music perception and even music itself. Recent advances in the application of probability theory to other domains of cognitive modeling, coupled with new evidence and theoretical insights about the working of the musical mind, have laid the groundwork for more fruitful investigations. Temperley proposes computational models for two basic cognitive processes, the perception of key and the perception of meter, using techniques of Bayesian probabilistic modeling. Drawing on his own research and surveying recent work by others, Temperley explores a range of further issues in music and probability, including transcription, phrase perception, pattern perception, harmony, improvisation, and musical styles.Music and Probability--the first full-length book to explore the application of probabilistic techniques to musical issues--includes a concise survey of probability theory, with simple examples and a discussion of its application in other domains. Temperley relies most heavily on a Bayesian approach, which not only allows him to model the perception of meter and tonality but also sheds light on such perceptual processes as error detection, expectation, and pitch identification. Bayesian techniques also provide insights into such subtle and advanced issues as musical ambiguity, tension, and "grammaticality," and lead to interesting and novel predictions about compositional practice and differences between musical styles. View full abstract»

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      Probabilistic Foundations and Background

      Page(s): 7 - 22
      Copyright Year: 2007

      MIT Press eBook Chapters

      This chapter contains section titled: 2.1 Elementary Probability, 2.2 Conditional Probability and Bayes' Rule, 2.3 Other Probabilistic Concepts, 2.4 Early Work on Music and Probability View full abstract»

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      Melody I: The Rhythm Model

      Page(s): 23 - 47
      Copyright Year: 2007

      MIT Press eBook Chapters

      This chapter contains section titled: 3.1 Rhythm and Meter, 3.2 Previous Models of Meter Perception, 3.3 A Probabilistic Rhythm Model, 3.4 The Generative Process, 3.5 The Meter-Finding Process, 3.6 Testing the Model on Meter-Finding, 3.7 Problems and Possible Improvements View full abstract»

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      Melody II: The Pitch Model

      Page(s): 49 - 64
      Copyright Year: 2007

      MIT Press eBook Chapters

      This chapter contains section titled: 4.1 Previous Models of Key-Finding, 4.2 The Pitch Model, 4.3 Testing the Model on Key-Finding View full abstract»

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      Melody III: Expectation and Error Detection

      Page(s): 65 - 78
      Copyright Year: 2007

      MIT Press eBook Chapters

      This chapter contains section titled: 5.1 Calculating the Probability of a Melodic Surface, 5.2 Pitch Expectation, 5.3 Rhythmic Expectation, 5.4 Error Detection, 5.5 Further Issues View full abstract»

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      Melody III: Expectation and Error Detection

      Page(s): 79 - 98
      Copyright Year: 2007

      MIT Press eBook Chapters

      This chapter contains section titled: 6.1 A Pitch-Class-Set Approach to Key-Finding, 6.2 The Generative Process, 6.3 The Key-Finding Process, 6.4 Comparing Distributional Models of Key-Finding, 6.5 Further Issues in Key-Finding View full abstract»

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      Applications of the Polyphonic Key-Finding Model

      Page(s): 99 - 137
      Copyright Year: 2007

      MIT Press eBook Chapters

      This chapter contains section titled: 7.1 Key Relations, 7.2 Tonalness, 7.3 Tonal Ambiguity and Clarity, 7.4 Another Look at Major and Minor, 7.5 Ambiguous Pitch-Collections in Common-Practice Music, 7.6 Explaining Common Strategies of Tonal Harmony View full abstract»

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      Bayesian Models of Other Aspects of Music

      Page(s): 139 - 158
      Copyright Year: 2007

      MIT Press eBook Chapters

      This chapter contains section titled: 8.1 Probabilistic Transcription Models, 8.2 Bod: The Perception of Phrase Structure, 8.3 Raphael and Stoddard: Harmonic Analysis, 8.4 Mavromatis: Modeling Greek Chant Improvisation, 8.5 Saffran et al.: Statistical Learning of Melodic Patterns View full abstract»

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      Style and Composition

      Page(s): 159 - 179
      Copyright Year: 2007

      MIT Press eBook Chapters

      This chapter contains section titled: 9.1 Some Simple Cross-Entropy Experiments, 9.2 Modeling Stylistic Differences, 9.3 Testing Schenkerian Theory View full abstract»

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      Communicative Pressure

      Page(s): 181 - 207
      Copyright Year: 2007

      MIT Press eBook Chapters

      This chapter contains section titled: 10.1 Communicative Pressure in Rules of Voice-Leading, 10.2 The Syncopation- Rubato Trade-Off, 10.3 Other Examples of Communicative Pressure in Rhythm, 10.4 “Trading Relationships”, 10.5 Low-Probability Events in Constrained Contexts, 10.6 Conclusions View full abstract»

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      Notes

      Page(s): 209 - 223
      Copyright Year: 2007

      MIT Press eBook Chapters

      This chapter contains section titled: Chapter 2, Chapter 3, Chapter 4, Chapter 5, Chapter 6, Chapter 7, Chapter 8, Chapter 9, Chapter 10 View full abstract»

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      References

      Page(s): 225 - 235
      Copyright Year: 2007

      MIT Press eBook Chapters

      In Music and Probability, David Temperley explores issues in music perception and cognition from a probabilistic perspective. The application of probabilistic ideas to music has been pursued only sporadically over the past four decades, but the time is ripe, Temperley argues, for a reconsideration of how probabilities shape music perception and even music itself. Recent advances in the application of probability theory to other domains of cognitive modeling, coupled with new evidence and theoretical insights about the working of the musical mind, have laid the groundwork for more fruitful investigations. Temperley proposes computational models for two basic cognitive processes, the perception of key and the perception of meter, using techniques of Bayesian probabilistic modeling. Drawing on his own research and surveying recent work by others, Temperley explores a range of further issues in music and probability, including transcription, phrase perception, pattern perception, harmony, improvisation, and musical styles.Music and Probability--the first full-length book to explore the application of probabilistic techniques to musical issues--includes a concise survey of probability theory, with simple examples and a discussion of its application in other domains. Temperley relies most heavily on a Bayesian approach, which not only allows him to model the perception of meter and tonality but also sheds light on such perceptual processes as error detection, expectation, and pitch identification. Bayesian techniques also provide insights into such subtle and advanced issues as musical ambiguity, tension, and "grammaticality," and lead to interesting and novel predictions about compositional practice and differences between musical styles. View full abstract»

    • Full text access may be available. Click article title to sign in or learn about subscription options.

      Author index

      Page(s): 237 - 239
      Copyright Year: 2007

      MIT Press eBook Chapters

      In Music and Probability, David Temperley explores issues in music perception and cognition from a probabilistic perspective. The application of probabilistic ideas to music has been pursued only sporadically over the past four decades, but the time is ripe, Temperley argues, for a reconsideration of how probabilities shape music perception and even music itself. Recent advances in the application of probability theory to other domains of cognitive modeling, coupled with new evidence and theoretical insights about the working of the musical mind, have laid the groundwork for more fruitful investigations. Temperley proposes computational models for two basic cognitive processes, the perception of key and the perception of meter, using techniques of Bayesian probabilistic modeling. Drawing on his own research and surveying recent work by others, Temperley explores a range of further issues in music and probability, including transcription, phrase perception, pattern perception, harmony, improvisation, and musical styles.Music and Probability--the first full-length book to explore the application of probabilistic techniques to musical issues--includes a concise survey of probability theory, with simple examples and a discussion of its application in other domains. Temperley relies most heavily on a Bayesian approach, which not only allows him to model the perception of meter and tonality but also sheds light on such perceptual processes as error detection, expectation, and pitch identification. Bayesian techniques also provide insights into such subtle and advanced issues as musical ambiguity, tension, and "grammaticality," and lead to interesting and novel predictions about compositional practice and differences between musical styles. View full abstract»

    • Full text access may be available. Click article title to sign in or learn about subscription options.

      Subject index

      Page(s): 241 - 244
      Copyright Year: 2007

      MIT Press eBook Chapters

      In Music and Probability, David Temperley explores issues in music perception and cognition from a probabilistic perspective. The application of probabilistic ideas to music has been pursued only sporadically over the past four decades, but the time is ripe, Temperley argues, for a reconsideration of how probabilities shape music perception and even music itself. Recent advances in the application of probability theory to other domains of cognitive modeling, coupled with new evidence and theoretical insights about the working of the musical mind, have laid the groundwork for more fruitful investigations. Temperley proposes computational models for two basic cognitive processes, the perception of key and the perception of meter, using techniques of Bayesian probabilistic modeling. Drawing on his own research and surveying recent work by others, Temperley explores a range of further issues in music and probability, including transcription, phrase perception, pattern perception, harmony, improvisation, and musical styles.Music and Probability--the first full-length book to explore the application of probabilistic techniques to musical issues--includes a concise survey of probability theory, with simple examples and a discussion of its application in other domains. Temperley relies most heavily on a Bayesian approach, which not only allows him to model the perception of meter and tonality but also sheds light on such perceptual processes as error detection, expectation, and pitch identification. Bayesian techniques also provide insights into such subtle and advanced issues as musical ambiguity, tension, and "grammaticality," and lead to interesting and novel predictions about compositional practice and differences between musical styles. View full abstract»