Welcome to MML11

Attendees: Please see the Program for last-minute changes. Apologies for any confusion these changes cause.

MML 2011
  4th International Workshop on Machine Learning  and Music: Learning from Musical Structure

Held in Conjunction with the 25th Annual Conference on Neural Information Processing Systems (NIPS 2011)
Melia Sierra Nevada & Melia Sol y Nieve, Sierra Nevada, Spain
Saturday December 17, 2011


Motivation
With the current explosion and quick expansion of music in digital formats, and the computational power of modern systems, research on machine learning and music is gaining increasing popularity. 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. The focus of this workshop is on novel methods which take into account or benefit from musical structure. MML 2011 aims to build on the previous three successful MML editions, MML’08, MML’09 and MML’10.

Topic

It has been convincingly shown that many useful applications can be built using features derived from short musical snippets (chroma, MFCCs and related timbral features, augmented with tempo and beat representations). Given the great advances in these applications, higher level aspects of musical structure such as melody, harmony, phrasing and rhythm can now be given further attention, and we especially welcome contributions exploring these areas. The MML 2011 workshop intends to concentrate on machine learning algorithms employing higher level features and representations for content-based music processing.

Papers in all applications on music and machine learning are welcome, including but not limited to 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, beat tracking, and expressive performance modeling. Audio demonstrations are encouraged when indicated by the content of the paper.


Expected Attendees

The expected attendees are active researchers in machine learning and music who have special interest in content-based music processing. We believe that this is a timely workshop because there is an increasing interest in music processing using machine learning techniques in both the ML and music communities, and that the time is ripe to start extracting, modeling and making use of higher-level features of music.

Agenda

The workshop is planned to last one full day, and will feature paper and poster presentations, panel discussions and open discussions. The accepted contributions will be available from the workshop web page as soon as possible in order to encourage active discussion during the workshop. At the end of each paper session there will be time allocated for discussion. Each discussion will initially be focused on the research reported by the session contributions, and then generalized to the session general topic. At the end of the workshop there will be a dedicated session to discuss about the perspectives and future directions of content-based music processing.

Important Dates
Abstract Submission Deadline: 23:59 EST, Friday, October 14, 2011
Acceptance Notification: October 25, 2011
Workshop Date: Saturday 17 December, 2011

Submissions of Papers
We solicit 2-page abstracts reporting unpublished research. Abstracts need not to be anonymous. Submissions should include the title, authors' names, institutions and email addresses.
Style files are available here. Papers should be submitted in pdf format by email to: musml2011@gmail.com no later than 23:59 EST, Friday, October 14, 2011.

Registration
Workshop registration will be handled by the main conference. For information about registration please refer to: http://nips.cc/Conferences/2011/

Organizers
Rafael Ramirez, Universitat Pompeu Fabra, Spain
Darrell Conklin, University of the Basque Country, Spain
Douglas Eck, Google, USA
Ryan Rifkin, Google, USA