David Meredith is an Associate Professor in the Dept. of Architecture, Design and Media Technology at Aalborg University. He has Bachelor's and Master's degrees in natural sciences and music from the University of Cambridge and a D.Phil. from the Faculty of Music of the University of Oxford. His research focuses on algorithms for analysing musical structure. He developed the first practical algorithms for discovering repeated patterns in polyphonic music and the most accurate pitch spelling algorithm to date. He is the lead investigator at Aalborg University on the EU collaborative project, "Learning to Create" (Lrn2Cre8).

• Music Analysis by Computer Ontology and Epistemology.- The Harmonic Musical Surface and Two Novel Chord Representation Schemes.- Topological Structures in Computer-Aided Music Analysis.- Contextual Set-Class Analysis.- Computational Analysis of Musical Form.- Chord- and Note-Based Approaches to Voice Separation.- Analysing Symbolic Music with Probabilistic Grammars.- Interactive Melodic Analysis.- Implementing Methods for Analysing Music Based on Lerdahl and Jackendoff’s Generative Theory of Tonal Music.- An Algebraic Approach to Time-Span Reduction.- Automated Motivic Analysis An Exhaustive Approach Based on Closed and Cyclic Pattern Mining in Multidimensional Parametric Spaces.- A Wavelet-Based Approach to Pattern Discovery in Melodies.- Analysing Music with Point-Set Compression Algorithms.- Composer Classification Models for Music-Theory Building.- Contrast Pattern Mining in Folk Music Analysis.- Pattern and Antipattern Discovery in Ethiopian Bagana Songs.- Using Geometric Symbolic Fingerprinting to Discover Distinctive Patterns in Polyphonic Music Corpora.- Index.

"A surprisingly large number of people in the computing field are also skilled musicians; this book will certainly appeal to them. ... This volume is a collection of current papers that gives a good introduction to the current state of the field, popular techniques, and key challenges. ... This book is well organized, deep, sound in methodology, challenging, and fascinating. It should be accessible for those with knowledge in both areas: computational methods and music." (Creed Jones, Computing Reviews, computingreviews.com, June, 2016) "The book has 17 chapters contributed by some of the leading music researchers, who have collectively done a terrific job in addressing as many topics as possible in this interdisciplinary area. ... I would strongly recommend this lucidly edited volume to all music researchers, as well as to students of music theory and analysis. It will also be useful to those interested in music technology." (Soubhik Chakraborty, Computing Reviews, computingreviews.com, June, 2016)

David Meredith is an Associate Professor in the Dept. of Architecture, Design and Media Technology at Aalborg University. He has Bachelor's and Master's degrees in natural sciences and music from the University of Cambridge and a D.Phil. from the Faculty of Music of the University of Oxford. His research focuses on algorithms for analysing musical structure. He developed the first practical algorithms for discovering repeated patterns in polyphonic music and the most accurate pitch spelling algorithm to date. He is the lead investigator at Aalborg University on the EU collaborative project, "Learning to Create" (Lrn2Cre8).

• Music Analysis by Computer Ontology and Epistemology.- The Harmonic Musical Surface and Two Novel Chord Representation Schemes.- Topological Structures in Computer-Aided Music Analysis.- Contextual Set-Class Analysis.- Computational Analysis of Musical Form.- Chord- and Note-Based Approaches to Voice Separation.- Analysing Symbolic Music with Probabilistic Grammars.- Interactive Melodic Analysis.- Implementing Methods for Analysing Music Based on Lerdahl and Jackendoff’s Generative Theory of Tonal Music.- An Algebraic Approach to Time-Span Reduction.- Automated Motivic Analysis An Exhaustive Approach Based on Closed and Cyclic Pattern Mining in Multidimensional Parametric Spaces.- A Wavelet-Based Approach to Pattern Discovery in Melodies.- Analysing Music with Point-Set Compression Algorithms.- Composer Classification Models for Music-Theory Building.- Contrast Pattern Mining in Folk Music Analysis.- Pattern and Antipattern Discovery in Ethiopian Bagana Songs.- Using Geometric Symbolic Fingerprinting to Discover Distinctive Patterns in Polyphonic Music Corpora.- Index.

"A surprisingly large number of people in the computing field are also skilled musicians; this book will certainly appeal to them. ... This volume is a collection of current papers that gives a good introduction to the current state of the field, popular techniques, and key challenges. ... This book is well organized, deep, sound in methodology, challenging, and fascinating. It should be accessible for those with knowledge in both areas: computational methods and music." (Creed Jones, Computing Reviews, computingreviews.com, June, 2016) "The book has 17 chapters contributed by some of the leading music researchers, who have collectively done a terrific job in addressing as many topics as possible in this interdisciplinary area. ... I would strongly recommend this lucidly edited volume to all music researchers, as well as to students of music theory and analysis. It will also be useful to those interested in music technology." (Soubhik Chakraborty, Computing Reviews, computingreviews.com, June, 2016)

David Meredith is an Associate Professor in the Dept. of Architecture, Design and Media Technology at Aalborg University. He has Bachelor's and Master's degrees in natural sciences and music from the University of Cambridge and a D.Phil. from the Faculty of Music of the University of Oxford. His research focuses on algorithms for analysing musical structure. He developed the first practical algorithms for discovering repeated patterns in polyphonic music and the most accurate pitch spelling algorithm to date. He is the lead investigator at Aalborg University on the EU collaborative project, "Learning to Create" (Lrn2Cre8).

• Music Analysis by Computer Ontology and Epistemology.- The Harmonic Musical Surface and Two Novel Chord Representation Schemes.- Topological Structures in Computer-Aided Music Analysis.- Contextual Set-Class Analysis.- Computational Analysis of Musical Form.- Chord- and Note-Based Approaches to Voice Separation.- Analysing Symbolic Music with Probabilistic Grammars.- Interactive Melodic Analysis.- Implementing Methods for Analysing Music Based on Lerdahl and Jackendoff’s Generative Theory of Tonal Music.- An Algebraic Approach to Time-Span Reduction.- Automated Motivic Analysis An Exhaustive Approach Based on Closed and Cyclic Pattern Mining in Multidimensional Parametric Spaces.- A Wavelet-Based Approach to Pattern Discovery in Melodies.- Analysing Music with Point-Set Compression Algorithms.- Composer Classification Models for Music-Theory Building.- Contrast Pattern Mining in Folk Music Analysis.- Pattern and Antipattern Discovery in Ethiopian Bagana Songs.- Using Geometric Symbolic Fingerprinting to Discover Distinctive Patterns in Polyphonic Music Corpora.- Index.

"A surprisingly large number of people in the computing field are also skilled musicians; this book will certainly appeal to them. ... This volume is a collection of current papers that gives a good introduction to the current state of the field, popular techniques, and key challenges. ... This book is well organized, deep, sound in methodology, challenging, and fascinating. It should be accessible for those with knowledge in both areas: computational methods and music." (Creed Jones, Computing Reviews, computingreviews.com, June, 2016) "The book has 17 chapters contributed by some of the leading music researchers, who have collectively done a terrific job in addressing as many topics as possible in this interdisciplinary area. ... I would strongly recommend this lucidly edited volume to all music researchers, as well as to students of music theory and analysis. It will also be useful to those interested in music technology." (Soubhik Chakraborty, Computing Reviews, computingreviews.com, June, 2016)