Subject: New book from Springer on Computational Music Analysis From: David Meredith <dave@xxxxxxxx> Date: Fri, 30 Oct 2015 18:36:24 +0100 List-Archive:<http://lists.mcgill.ca/scripts/wa.exe?LIST=AUDITORY>> This message is in MIME format. Since your mail reader does not understand this format, some or all of this message may not be legible. --B_3529074988_10322578 Content-type: text/plain; charset="ISO-8859-1" Content-transfer-encoding: quoted-printable [Apologies for cross-posting!] Dear list members, I'm delighted to announce the online publication of a new book from Springer, entitled "Computational Music Analysis". The book is available through Springerlink at the following URL: http://link.springer.com/book/10.1007/978-3-319-25931-4 The book is also available for pre-order on Amazon at the following URLs: http://www.amazon.co.uk/Computational-Music-Analysis-David-Meredith/dp/3319= 2 59296 http://www.amazon.com/Computational-Music-Analysis-David-Meredith/dp/331925= 9 296 http://www.amazon.de/Computational-Music-Analysis-David-Meredith/dp/3319259= 2 96 http://www.amazon.fr/Computational-Music-Analysis-David-Meredith/dp/3319259= 2 96 The book contains 17 chapters representing the following general areas: - Methodology - Chords and pitch class sets - Parsing large-scale structure: Form and voice-separation - Grammars and hierarchical structure - Motivic and thematic analysis - Classification and distinctive patterns I include below the text from the back cover, along with a list of chapters= . Kind regards, David Meredith __________ >From the back cover: This book provides an in-depth introduction and overview of current researc= h in computational music analysis. Its seventeen chapters, written by leading researchers, collectively represent the diversity as well as the technical and philosophical sophistication of the work being done today in this intensely interdisciplinary field. A broad range of approaches are presented, employing techniques originating in disciplines such as linguistics, information theory, information retrieval, pattern recognition= , machine learning, topology, algebra and signal processing. Many of the methods described draw on well-established theories in music theory and analysis, such as Forte's pitch-class set theory, Schenkerian analysis, the methods of semiotic analysis developed by Ruwet and Nattiez, and Lerdahl an= d Jackendoff's Generative Theory of Tonal Music. The book is divided into six parts, covering methodological issues, harmoni= c and pitch-class set analysis, form and voice-separation, grammars and hierarchical reduction, motivic analysis and pattern discovery and, finally= , classification and the discovery of distinctive patterns. As a detailed and up-to-date picture of current research in computational music analysis, the book provides an invaluable resource for researchers, teachers and students in music theory and analysis, computer science, music information retrieval and related disciplines. It also provides a state-of-the-art reference for practitioners in the music technology industry. ___________ List of chapters (please excuse lack of accents) Part I Methodology 1 Music Analysis by Computer: Ontology and Epistemology Alan Marsden Part II Chords and Pitch Class Sets 2 The Harmonic Musical Surface and Two Novel Chord Representation Schemes Emilios Cambouropoulos 3 Topological Structures in Computer-Aided Music Analysis Louis Bigo and Moreno Andreatta 4 Contextual Set-Class Analysis Agustin Martorell and Emilia Gomez Part III Parsing Large-Scale Structure: Form and Voice-Separation 5 Computational Analysis of Musical Form Mathieu Giraud, Richard Groult, and Florence Leve 6 Chord- and Note-Based Approaches to Voice Separation Tillman Weyde and Reinier de Valk Part IV Grammars and Hierarchical Structure 7 Analysing Symbolic Music with Probabilistic Grammars Samer Abdallah, Nicolas Gold, and Alan Marsden 8 Interactive Melodic Analysis David Rizo, Placido R. Illescas, and Jose M. Inesta 9 Implementing Methods for Analysing Music Based on Lerdahl and Jackendoff=B9= s Generative Theory of Tonal Music Masatoshi Hamanaka, Keiji Hirata, and Satoshi Tojo 10 An Algebraic Approach to Time-Span Reduction Keiji Hirata, Satoshi Tojo, and Masatoshi Hamanaka Part V Motivic and Thematic Analysis 11 Automated Motivic Analysis: An Exhaustive Approach Based on Closed and Cyclic Pattern Mining in Multidimensional Parametric Spaces Olivier Lartillot 12 A Wavelet-Based Approach to Pattern Discovery in Melodies Gissel Velarde, David Meredith, and Tillman Weyde 13 Analysing Music with Point-Set Compression Algorithms David Meredith Part VI Classification and Distinctive Patterns 14 Composer Classification Models for Music-Theory Building Dorien Herremans, David Martens, and Kenneth Sorensen 15 Contrast Pattern Mining in Folk Music Analysis Kerstin Neubarth and Darrell Conklin 16 Pattern and Antipattern Discovery in Ethiopian Bagana Songs Darrell Conklin and Stephanie Weisser 17 Using Geometric Symbolic Fingerprinting to Discover Distinctive Patterns in Polyphonic Music Corpora Tom Collins, Andreas Arzt, Harald Frostel, and Gerhard Widmer --B_3529074988_10322578 Content-type: text/html; charset="ISO-8859-1" Content-transfer-encoding: quoted-printable <html><head></head><body style=3D"word-wrap: break-word; -webkit-nbsp-mode: s= pace; -webkit-line-break: after-white-space; font-family: Calibri, sans-seri= f; font-size: 16px; color: rgb(0, 0, 0);"><div>[Apologies for cross-posting!= ]</div><div><br></div><div>Dear list members,</div><div><br></div><div>I'm d= elighted to announce the online publication of a new book from Springer, ent= itled "Computational Music Analysis". The book is available through Springer= link at the following URL:</div><div><br></div><div><a href=3D"http://link.spr= inger.com/book/10.1007/978-3-319-25931-4">http://link.springer.com/book/10.1= 007/978-3-319-25931-4</a></div><div><br></div><div>The book is also availabl= e for pre-order on Amazon at the following URLs:</div><div><br></div><div><a= href=3D"http://www.amazon.co.uk/Computational-Music-Analysis-David-Meredith/d= p/3319259296">http://www.amazon.co.uk/Computational-Music-Analysis-David-Mer= edith/dp/3319259296</a></div><div><a href=3D"http://www.amazon.com/Computation= al-Music-Analysis-David-Meredith/dp/3319259296">http://www.amazon.com/Comput= ational-Music-Analysis-David-Meredith/dp/3319259296</a></div><div><a href=3D"h= ttp://www.amazon.de/Computational-Music-Analysis-David-Meredith/dp/331925929= 6">http://www.amazon.de/Computational-Music-Analysis-David-Meredith/dp/33192= 59296</a></div><div><a href=3D"http://www.amazon.fr/Computational-Music-Analys= is-David-Meredith/dp/3319259296">http://www.amazon.fr/Computational-Music-An= alysis-David-Meredith/dp/3319259296</a></div><div><br></div><div>The book co= ntains 17 chapters representing the following general areas:</div><div><br><= /div><div>- Methodology</div><div>- Chords and pitch class sets</div><div>- = Parsing large-scale structure: Form and voice-separation</div><div>- Grammar= s and hierarchical structure</div><div>- Motivic and thematic analysis</div>= <div>- Classification and distinctive patterns</div><div><br></div><div>I in= clude below the text from the back cover, along with a list of chapters.</di= v><div><br></div><div>Kind regards,</div><div>David Meredith</div><div><br><= /div><div>__________</div><div>From the back cover:</div><div><br></div><div= >This book provides an in-depth introduction and overview of current researc= h in computational music analysis. Its seventeen chapters, written by leadin= g researchers, collectively represent the diversity as well as the technical= and philosophical sophistication of the work being done today in this inten= sely interdisciplinary field. A broad range of approaches are presented, emp= loying techniques originating in disciplines such as linguistics, informatio= n theory, information retrieval, pattern recognition, machine learning, topo= logy, algebra and signal processing. Many of the methods described draw on w= ell-established theories in music theory and analysis, such as Forte's pitch= -class set theory, Schenkerian analysis, the methods of semiotic analysis de= veloped by Ruwet and Nattiez, and Lerdahl and Jackendoff's Generative Theory= of Tonal Music.</div><div><br></div><div>The book is divided into six parts= , covering methodological issues, harmonic and pitch-class set analysis, for= m and voice-separation, grammars and hierarchical reduction, motivic analysi= s and pattern discovery and, finally, classification and the discovery of di= stinctive patterns.</div><div><br></div><div>As a detailed and up-to-date pi= cture of current research in computational music analysis, the book provides= an invaluable resource for researchers, teachers and students in music theo= ry and analysis, computer science, music information retrieval and related d= isciplines. It also provides a state-of-the-art reference for practitioners = in the music technology industry.</div><div><br></div><div>___________</div>= <div><br></div><div>List of chapters (please excuse lack of accents)</div><d= iv><br></div><div>Part I Methodology</div><div><br></div><div>1 Music Analys= is by Computer: Ontology and Epistemology</div><div>Alan Marsden</div><div><= br></div><div>Part II Chords and Pitch Class Sets</div><div><br></div><div>2= The Harmonic Musical Surface and Two Novel Chord Representation Schemes</di= v><div>Emilios Cambouropoulos</div><div><br></div><div>3 Topological Structu= res in Computer-Aided Music Analysis</div><div>Louis Bigo and Moreno Andreat= ta</div><div><br></div><div>4 Contextual Set-Class Analysis</div><div>Agusti= n Martorell and Emilia Gomez</div><div><br></div><div>Part III Parsing Large= -Scale Structure: Form and Voice-Separation</div><div><br></div><div>5 Compu= tational Analysis of Musical Form</div><div>Mathieu Giraud, Richard Groult, = and Florence Leve</div><div><br></div><div>6 Chord- and Note-Based Approache= s to Voice Separation</div><div>Tillman Weyde and Reinier de Valk</div><div>= <br></div><div>Part IV Grammars and Hierarchical Structure</div><div><br></d= iv><div>7 Analysing Symbolic Music with Probabilistic Grammars</div><div>Sam= er Abdallah, Nicolas Gold, and Alan Marsden</div><div><br></div><div>8 Inter= active Melodic Analysis</div><div>David Rizo, Placido R. Illescas, and Jose = M. Inesta</div><div><br></div><div>9 Implementing Methods for Analysing Musi= c Based on Lerdahl and Jackendoff’s Generative Theory of Tonal Music</= div><div>Masatoshi Hamanaka, Keiji Hirata, and Satoshi Tojo</div><div><br></= div><div>10 An Algebraic Approach to Time-Span Reduction</div><div>Keiji Hir= ata, Satoshi Tojo, and Masatoshi Hamanaka</div><div><br></div><div>Part V Mo= tivic and Thematic Analysis</div><div><br></div><div>11 Automated Motivic An= alysis: An Exhaustive Approach Based on Closed and Cyclic Pattern Mining in = Multidimensional Parametric Spaces</div><div>Olivier Lartillot</div><div><br= ></div><div>12 A Wavelet-Based Approach to Pattern Discovery in Melodies</di= v><div>Gissel Velarde, David Meredith, and Tillman Weyde</div><div><br></div= ><div>13 Analysing Music with Point-Set Compression Algorithms</div><div>Dav= id Meredith</div><div><br></div><div>Part VI Classification and Distinctive = Patterns</div><div><br></div><div>14 Composer Classification Models for Musi= c-Theory Building</div><div>Dorien Herremans, David Martens, and Kenneth Sor= ensen</div><div><br></div><div>15 Contrast Pattern Mining in Folk Music Anal= ysis</div><div>Kerstin Neubarth and Darrell Conklin</div><div><br></div><div= >16 Pattern and Antipattern Discovery in Ethiopian Bagana Songs</div><div>Da= rrell Conklin and Stephanie Weisser</div><div><br></div><div>17 Using Geomet= ric Symbolic Fingerprinting to Discover Distinctive Patterns in Polyphonic M= usic Corpora</div><div>Tom Collins, Andreas Arzt, Harald Frostel, and Gerhar= d Widmer</div><div><br></div><div><br></div><div><br></div><div><br></div></= body></html> --B_3529074988_10322578--