• June 19, 2019

PDF | review of David Temperley’s “Music and Probability”. Cambridge, Massachusetts: MIT Press, , ISBN (hardcover) $ Music and probability / David Temperley. p. cm. Includes bibliographical references and index. Contents: Probabilistic foundations and background— Melody I. So, David Temperley is right to say, in the introduction to his new With Music and Probability, Temperley sets out to fulfill two main tasks: to give an introduction.

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If flags can be used, they are placed after the program name, possibly with a number after them: A well written text, exploring a multi-faceted approach to music-theoretical thinking.

Music and Probability – David Temperley

The emphasis is on Bayesian methods and the result is a firm empirical grounding for music theory. Amazon Giveaway allows you to run promotional giveaways in order to create buzz, reward your audience, and attract new followers and customers. Don’t have a Kindle?

The book reviews various models of music perception tested with a Baye’s Theorem. A very accessible text, that explores deeper theoretical concepts concerning the syntax of music. A History of the Cognitive Revolution. A Polyphonic Key-Finding Model 79 6.

Music and Probability

Use the program compare-na to compare your note-address list with the correct one. This document and all portions thereof are protected by U. The section of rhythm perception is dense and takes several readings – and for ans many diagrams – to understand just what in the heck he was doing.

Hence, an interested reader even one without a background in probability will learn tempedley about mathematics and probabiity psychological modeling of music perception and creation. Kindle Edition Verified Purchase. Chapter eight surveys some recent work by other authors in which probabilistic methods are applied to a variety of problems in music perception and cognition: The following is the table of contents: Temperley proposes computational models for two basic cognitive processes, the perception of key and the perception of meter, using techniques of Bayesian probabilistic modeling.


Music, the Arts, and Ideas. Once music scholars become accustomed to a Bayesian approach to music, they will find the reliability and scope of the models to be of great assistance. Drawing temper,ey 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. See below for further information.

In particular they lead very naturally to ways of identifying the probability of actual note patterns. English Choose a language for shopping. Amazon Rapids Fun stories for kids on the go. University of Chicago Press. The book is self-explanatory. Top Reviews Most recent Top Reviews.

But from there, you step off the deep end as he tries to explain the various models used for music perception such as how the perceiver comes to detect the particular rhythm or key of a piece of music. Chapter nine considers the idea of construing probabilistic models as descriptions of musical styles and thus as hypotheses about cognitive processes involved in composition. As mentioned above, chapter 2 is a basic introduction to conditional probability, and other mathematical concepts are presented and explained when needed.

Exploring the application of Bayesian probabilistic modeling techniques to musical issues, including the perception of key and meter. Review As he did in The Cognition of Basic Musical StructuresTemperley here challenges the frontiers of the definition of music theory and cognition.

Expectation and Error Detection 65 5. Probabilistic Foundations and Background 7 2.

Temperley needed to rethink carefully how to present some rather esoteric models in a way that would be accessible to the average intelligent reader. Our knowledge of probability comes, in large part, from regularities in the environment.

A good example is the way Temperley amplifies his Bayesian models to comprehend how humans are able to identify individual notes and musical phrases, both of which are significant issues in a realistic cognitive model of music perception.

This in turn provides a davjd of modeling cognitive processes such as error detection, expectation, and pitch identification, as well as more subtle musical phenomena progability as musical ambiguity, tension, and “tonalness”.


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. A Geometry of Music: With regard to both meter and key, the models proposed are not merely models of information eavid, but also shed light on other aspects of perception.

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David Temperley

Amazon Inspire Digital Educational Resources. Get fast, free shipping with Amazon Prime. I propose computational models for two basic cognitive processes, the perception of key and the perception of meter, using techniques of Bayesian probabilistic modeling.

Cross-entropy shows in a quantitative way how well a model predicts a etmperley of data. Finally, Temperley introduces his models, which are always justified and compared to the other established models.

Temperley’s book is timely and will be a major contribution to the field of music cognition. Producers of communication are sensitive to, and affected by, its probabilistic nature.

Tonalness is used to explain human perception and judgments of key relations, and to solve some problems of ambiguity. He explains how probability is used to detect pitch or rhythm, and argues that in order to state that a certain composition is within a specific style we generate probabilities from different models, and assign the one prrobability higher probability.

Rather, it shapes his theory about how our mind actually perceives specific musical elements such as dynamics, rhythm, chords, melody, harmony, and so on.

Use the program tally-na if desired to take a series of outputs from compare-na and combine them.