Exploring the application of Bayesian probabilistic modeling techniques to musical issues, including the perception of key and meter.

In this book, David Temperley addresses a fundamental question about music cognition: how do we extract basic kinds of musical information, such as meter, phrase structure, counterpoint, pitch spelling, harmony, and key from music as we hear it? Taking a computational approach, Temperley develops models for generating these aspects of musical structure. The models he proposes are based on preference rules, which are criteria for evaluating a possible structural analysis of a piece of music. A preference rule system evaluates many possible interpretations and chooses the one that best satisfies the rules. After an introductory chapter, Temperley presents preference rule systems for generating six basic kinds of musical structure: meter, phrase structure, contrapuntal structure, harmony, and key, as well as pitch spelling (the labeling of pitch events with spellings such as A flat or G sharp). He suggests that preference rule systems not only show how musical structures are inferred, but also shed light on other aspects of music. He substantiates this claim with discussions of musical ambiguity, retrospective revision, expectation, and music outside the Western canon (rock and traditional African music). He proposes a framework for the description of musical styles based on preference rule systems and explores the relevance of preference rule systems to higher-level aspects of music, such as musical schemata, narrative and drama, and musical tension.

This textbook differs from others in the field in that it has been prepared very much with students and their needs in mind, having been classroom tested over many years. It is a true “learner’s book” made for students who require a deeper understanding of probability and statistics. It presents the fundamentals of the subject along with concepts of probabilistic modelling, and the process of model selection, verification and analysis. Furthermore, the inclusion of more than 100 examples and 200 exercises (carefully selected from a wide range of topics), along with a solutions manual for instructors, means that this text is of real value to students and lecturers across a range of engineering disciplines. Key features: Presents the fundamentals in probability and statistics along with relevant applications. Explains the concept of probabilistic modelling and the process of model selection, verification and analysis. Definitions and theorems are carefully stated and topics rigorously treated. Includes a chapter on regression analysis. Covers design of experiments. Demonstrates practical problem solving throughout the book with numerous examples and exercises purposely selected from a variety of engineering fields. Includes an accompanying online Solutions Manual for instructors containing complete step-by-step solutions to all problems.

An intuitive, yet precise introduction to probability theory, stochastic processes, statistical inference, and probabilistic models used in science, engineering, economics, and related fields. This is the currently used textbook for an introductory probability course at the Massachusetts Institute of Technology, attended by a large number of undergraduate and graduate students, and for a leading online class on the subject. The book covers the fundamentals of probability theory (probabilistic models, discrete and continuous random variables, multiple random variables, and limit theorems), which are typically part of a first course on the subject. It also contains a number of more advanced topics, including transforms, sums of random variables, a fairly detailed introduction to Bernoulli, Poisson, and Markov processes, Bayesian inference, and an introduction to classical statistics. The book strikes a balance between simplicity in exposition and sophistication in analytical reasoning. Some of the more mathematically rigorous analysis is explained intuitively in the main text, and then developed in detail (at the level of advanced calculus) in the numerous solved theoretical problems.

This work aims to address the historical development of the great Indian raga tradition, enhanced by computational approaches, and to use computational strategies to analyze aspects of contemporary Hindustani classical music (HCM). It is divided into two parts with Part 1 focusing on the history and aesthetics of HCM and Part 2 covering its computational aspects. The historical development of HCM in the ancient, medieval and modern periods; its terms and genre; and its Khayal gharanas are covered in Part 1. The subtopics include essential concepts such as raga, tala, shruti, thaat, gharana, khayal, dhrupad, thumri, tappa, etc. Part 2 covers the state-of-the-art in computational musicology, raga analysis and song analysis using statistics. The subtopics include statistical modeling, inter onset interval, note duration analysis, pitch movement between the notes, rate of change of pitch (pitch velocity) and probabilistic analysis of musical notes. The author concludes the work with reflecting on the lives of a few renowned musicians and musicologists with an account of hilarious moments taken from their lives to excite the reader to know more about HCM. This book would be useful for musicians, musicologists, researchers in music history, aesthetics, computational musicology, and advanced undergraduate and postgraduate students of music and musicology.

Introducing many innovations in content and methods, this book involves the foundations, basic concepts, and fundamental results of probability theory. Geared toward readers seeking a firm basis for study of mathematical statistics or information theory, it also covers the mathematical notions of experiments and independence. 1970 edition.