[AUDITORY] CFP: MML 2017 10th International Workshop on Machine Learning and Music (Rafael Ramirez Melendez )


Subject: [AUDITORY] CFP: MML 2017 10th International Workshop on Machine Learning and Music
From:    Rafael Ramirez Melendez  <rafael.ramirez@xxxxxxxx>
Date:    Thu, 18 May 2017 12:52:50 +0200
List-Archive:<http://lists.mcgill.ca/scripts/wa.exe?LIST=AUDITORY>

This is a multi-part message in MIME format. --------------87101807F70A25A5E89EB18B Content-Type: text/plain; charset=utf-8; format=flowed Content-Transfer-Encoding: quoted-printable X-MIME-Autoconverted: from 8bit to quoted-printable by edgeum1.it.mcgill.ca id v4IAqvf1011678 [apologies for multiple postings] *Call for Papers* *MML 2016: 9th International Workshop on Machine Learning and Music *http://musml.weebly.com October 6, 2017 Barcelona MOTIVATION Machine learning has permeated nearly every area of music informatics,=20 driven by a profusion of recordings available in digital audio formats,=20 steady improvements to the accessibility and quality of symbolic=20 corpora, availability of powerful algorithms in standard machine=20 learning toolboxes, and theoretical advances in machine learning and=20 data mining. As complexity of the problems investigated by researchers=20 on machine learning and music increases, there is a need to develop new=20 algorithms and methods to solve these problems. As a consequence,=20 research on machine learning and music is an active and growing field=20 reflected in international meetings such as the International Workshops=20 on Machine Learning and Music (MML): MML2008 (Helsinki, Finland),=20 MML2009 (Bled, Slovenia), MML2010 (Florence, Italy), MML2011 (Sierra=20 Nevada, Spain), MML2012 (Edinburgh, Scotland), MML2013 (Prague, Czech=20 Republic), MML2014 (Barcelona, Spain), MML2015 (Vancouver, Canada), and=20 MML2016 (Riva del Garda, Italy). SPECIAL THEME: INTELLIGENT MUSIC LEARNING SYSTEMS Machine learning holds great potential for enhancing music learning.=20=20 For MML2017, in addition to general topics in music and machine=20 learning, we warmly welcome contributions describing applications of=20 machine learning for technology-enhanced music learning. TOPICS Papers in all applications on music and machine learning are welcome,=20 including but not limited to * intelligent music learning systems * machine learning applications to music learning * automatic classification of music (audio and MIDI) * style-based interpreter recognition * automatic composition and improvisation * music recommender systems, genre and tag prediction * score alignment * polyphonic pitch detection * chord extraction * pattern discovery * music analysis * beat tracking * expressive performance modeling * similarity Audio demonstrations are encouraged when indicated by the content of the=20 paper. IMPORTANT DATES Paper Submission Deadline: July 3, 2017 Acceptance Notification: July 30, 2017 Final versions due: August 10, 2017 Workshop Date: October 6, 2017 SUBMISSIONS OF PAPERS Papers of up to 5 printed pages in LNCS format are welcome. Submissions=20 will be evaluated according to their originality and relevance to the=20 workshop, and should include author names, affiliations, contact=20 information, and an abstract of 60-100 words. Contributions should be in=20 PDF format and submitted by Easychair. LNCS format details can be found=20 here. REGISTRATION Registration to the Workshop will be free of charge. However, the=20 workshop has a limited number of places for non presenters, SPECIAL ISSUE A special issue of a journal based on selected papers from the workshop=20 is planned. ORGANISERS Rafael Ramirez, Universitat Pompeu Fabra, Spain Darrell Conklin, University of the Basque Country, Spain Jos=C3=A9 Manuel I=C3=B1esta, University of Alicante, Spain PROGRAMME COMMITTEE Darrell Conklin (University of the Basque Country, Spain) Dorien Herremans (Queen Mary University of London, UK) Emmanouil Benetos (Queen Mary University of London, UK) Gualtiero Volpe (University of Genoa, Italy) Hendrik Purwins (Aalborg University, Denmark) Kerstin Neubarth (Canterbury Christ Church University, UK) Jos=C3=A9 Manuel I=C3=B1esta (University of Alicante, Spain) Maarten Grachten (Austrian Research Institute for Artificial=20 Intelligence, Austria) Maria Cristina Marinescu (Barcelona Supercomputing Centre, Spain) Miguel Molina (Imperial College London, UK) Peter van Kranenburg (Meertens Institute, Netherlands) Rafael Ramirez (Universitat Pompeu Fabra, Spain) Sergio Giraldo (Universitat Pompeu Fabra, Spain) Pierre Ponce de Leon (University of Alicante, Spain) Victor Padilla (International University of la Rioja, Spain) Zacharias Vamvakousis (Universitat Pompeu Fabra, Spain) SUPPORTED BY Picture Picture Picture --------------87101807F70A25A5E89EB18B Content-Type: multipart/related; boundary="------------3E41DD3018D17AFE7390DB2A" --------------3E41DD3018D17AFE7390DB2A Content-Type: text/html; charset=utf-8 Content-Transfer-Encoding: quoted-printable X-MIME-Autoconverted: from 8bit to quoted-printable by edgeum1.it.mcgill.ca id v4IAqvf1011678 <html> <head> <meta http-equiv=3D"content-type" content=3D"text/html; charset=3Dutf-8= "> </head> <body bgcolor=3D"#FFFFFF" text=3D"#000000"> <p>[apologies for multiple postings] </p> <div><br> </div> <div><b>Call for Papers</b></div> <div><br> </div> <b>MML 2016: 9th International Workshop on Machine Learning and Music<br> </b><!-- <a href=3D"http://musml.weebly.com"> -->http://musml.weebly.com <font color=3Dgray>[ musml. weebly. com ]</font> <!-- </a> --><br> October 6, 2017 Barcelona<br> <br> MOTIVATION<br> Machine learning has permeated nearly every area of music informatics, driven by a profusion of recordings available in digital audio formats, steady improvements to the accessibility and quality of symbolic corpora, availability of powerful algorithms in standard machine learning toolboxes, and theoretical advances in machine learning and data mining. As complexity of the problems investigated by researchers on machine learning and music increases, there is a need to develop new algorithms and methods to solve these problems. As a consequence, research on machine learning and music is an active and growing field reflected in international meetings such as the International Workshops on Machine Learning and Music (MML): MML2008 (Helsinki, Finland), MML2009 (Bled, Slovenia), MML2010 (Florence, Italy), MML2011 (Sierra Nevada, Spain), MML2012 (Edinburgh, Scotland), MML2013 (Prague, Czech Republic), MML2014 (Barcelona, Spain), MML2015 (Vancouver, Canada), and MML2016 (Riva del Garda, Italy).<br> <br> SPECIAL THEME: INTELLIGENT MUSIC LEARNING SYSTEMS<br> Machine learning holds great potential for enhancing music learning.=C2=A0 For MML2017, in addition to general topics in music and machine learning, we warmly welcome contributions describing applications of machine learning for technology-enhanced music learning. <br> <br> TOPICS<br> Papers in all applications on music and machine learning are welcome, including but not limited to<br> <br> <ul> <li>=C2=A0=C2=A0=C2=A0 intelligent music learning systems</li> <li>=C2=A0=C2=A0=C2=A0 machine learning applications to music learnin= g</li> <li>=C2=A0=C2=A0=C2=A0 automatic classification of music (audio and M= IDI)</li> <li>=C2=A0=C2=A0=C2=A0 style-based interpreter recognition</li> <li>=C2=A0=C2=A0=C2=A0 automatic composition and improvisation</li> <li>=C2=A0=C2=A0=C2=A0 music recommender systems, genre and tag predi= ction</li> <li>=C2=A0=C2=A0=C2=A0 score alignment</li> <li>=C2=A0=C2=A0=C2=A0 polyphonic pitch detection</li> <li>=C2=A0=C2=A0=C2=A0 chord extraction</li> <li>=C2=A0=C2=A0=C2=A0 pattern discovery</li> <li>=C2=A0=C2=A0=C2=A0 music analysis</li> <li>=C2=A0=C2=A0=C2=A0 beat tracking</li> <li>=C2=A0=C2=A0=C2=A0 expressive performance modeling</li> <li>=C2=A0=C2=A0=C2=A0 similarity</li> </ul> <br> Audio demonstrations are encouraged when indicated by the content of the paper.<br> <br> IMPORTANT DATES<br> Paper Submission Deadline: July 3, 2017<br> Acceptance Notification: July 30, 2017<br> Final versions due: August 10, 2017<br> Workshop Date: October 6, 2017<br> <br> SUBMISSIONS OF PAPERS<br> Papers of up to 5 printed pages in LNCS format are welcome. Submissions will be evaluated according to their originality and relevance to the workshop, and should include author names, affiliations, contact information, and an abstract of 60-100 words. Contributions should be in PDF format and submitted by Easychair. LNCS format details can be found here.<br> <br> REGISTRATION<br> Registration to the Workshop will be free of charge. However, the workshop has a limited number of places for non presenters,<br> <br> SPECIAL ISSUE<br> A special issue of a journal based on selected papers from the workshop is planned.<br> <br> ORGANISERS<br> Rafael Ramirez, Universitat Pompeu Fabra, Spain<br> Darrell Conklin, University of the Basque Country, Spain<br> Jos=C3=A9 Manuel I=C3=B1esta, University of Alicante, Spain<br> <br> PROGRAMME COMMITTEE<br> Darrell Conklin (University of the Basque Country, Spain)<br> Dorien Herremans (Queen Mary University of London, UK)<br> Emmanouil Benetos (Queen Mary University of London, UK)<br> Gualtiero Volpe (University of Genoa, Italy)<br> Hendrik Purwins (Aalborg University, Denmark)<br> Kerstin Neubarth (Canterbury Christ Church University, UK)<br> Jos=C3=A9 Manuel I=C3=B1esta (University of Alicante, Spain)<br> Maarten Grachten (Austrian Research Institute for Artificial Intelligence, Austria)<br> Maria Cristina Marinescu (Barcelona Supercomputing Centre, Spain)<br> Miguel Molina (Imperial College London, UK)<br> Peter van Kranenburg (Meertens Institute, Netherlands)<br> Rafael Ramirez (Universitat Pompeu Fabra, Spain)<br> Sergio Giraldo (Universitat Pompeu Fabra, Spain)<br> Pierre Ponce de Leon (University of Alicante, Spain)<br> Victor Padilla (International University of la Rioja, Spain)<br> Zacharias Vamvakousis (Universitat Pompeu Fabra, Spain)<br> <br> SUPPORTED BY<br> <meta http-equiv=3D"content-type" content=3D"text/html; charset=3Dutf-8= "> <meta http-equiv=3D"content-type" content=3D"text/html; charset=3Dutf-8= "> <img src=3D"cid:part2.E455C0AB.82AD06EF@xxxxxxxx" alt=3D"Picture" style=3D"width:151;max-width:100%"> <meta http-equiv=3D"content-type" content=3D"text/html; charset=3Dutf-8= "> <img src=3D"cid:part3.0FE397D0.82BE6867@xxxxxxxx" alt=3D"Picture" style=3D"max-width: 100%;" height=3D"54" width=3D"135"> <meta http-equiv=3D"content-type" content=3D"text/html; charset=3Dutf-8= "> <img src=3D"cid:part4.5777B0E1.D28ACE80@xxxxxxxx" alt=3D"Picture" 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maintained by:
DAn Ellis <dpwe@ee.columbia.edu>
Electrical Engineering Dept., Columbia University