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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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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 dvaid key of a piece of music. Highly recommended for all who have an interest in algorithmic composition. It is gratifying to see such first-rate work. After this introduction, the book presents relevant concepts as needed.

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According to this paradigm, minds are viewed as symbolic processors, and syntactic rules—rules that correlate with the form of information only, not its contents—were enough in principle to represent knowledge and the way humans think and solve problems Gardner Read more Read less.

Expectation and Error Detection 65 5.

If you are a muxic for this product, would you like to suggest updates through seller support? The author also makes use of concepts from information theory such as the idea of cross-entropy.

Cognitive Science and Human Experience. Thus the author invokes a number of concepts from probability theory and probabilistic modeling, relying most heavily on Bayes Rule, an axiom of probability.


Temperley, an accomplished and imaginative music theorist, knows the data of music to which he lucidly applies probabilistic modeling techniques. Probabilistic reasoning provides the glue davkd attaches theory to data.

Music and Probability (The MIT Press): David Temperley: : Books

In music perception, one is often confronted with a pattern of notes and wishes to know the underlying structure that gave rise to it. 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.

In particular they lead very naturally to ways of identifying the probability of actual note patterns. 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.

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 gen-add to generate note address list from a note-beat list. Explore the Home Gift Guide. Add all three to Cart Add all three to List.

Music and Probability – David Temperley

Temperley has made a seminal contribution to the emerging fields of empirical and cognitive musicology. A Polyphonic Key-Finding Model 79 6. By means of the Bayes rule, one is able to make inferences about a hidden variable that is related to a structure not directly accessible, based on knowledge about an observable variable. Music, the Arts, and Ideas. Although the process was thought to be equivalent in some sense to that of neural networks, it was impossible to develop an algorithm that could predict what the cognitive processes would be like.


Temperley proposes computational models for two basic cognitive processes, the perception of key and the perception of meter, using techniques of Bayesian probabilistic modeling. This in turn provides a way of modeling cognitive processes such as error detection, expectation, and pitch identification, as well as more subtle musical phenomena such as musical ambiguity, tension, and “tonalness”. One person found this helpful. A History of the Cognitive Revolution.

Its main goal, then, is to establish relationships between probability and musical style, in order to gain insight about music from a cognitive perspective.

The Rhythm Model 23 3.

David Temperley

Drawing on my xavid research and surveying recent work by others, I explore a range of further issues in music and probability, including transcription, phrase perception, pattern perception, harmony, improvisation, and musical styles. The aim of the book is commendable and the utilization of Baye’s Theorem seems appropriate.

His model is then compared with experimental results of how humans detect key in polyphonic music so as to show the robustness and cognitive reality of the model. The probabilkty straightforward task of detecting the spatial origin of a sound is a complex cognitive issue that requires several neural systems working together.

Harmonic Analysis 8. Tempefley by Sean Atkinson, Editorial Assistant. If music perception is largely probabilistic in nature this should not be surprising since probability pervades almost every aspect of mental life.