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.
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.
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.
The workshop is planned to last one full day, and will feature paper and poster presentations, panel discussions and open discussions (see proposed schedule below).
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.
Call for Papers
The Call for Papers can be found at:
Rafael Ramirez (Universitat Pompeu Fabra)
Darrell Conklin (University of the Basque Country)
Douglas Eck (Google Brain)
I’m a research scientist working on Magenta, an effort to generate music, video, images and text using machine intelligence. Magenta is part of the Google Brain team and is using TensorFlow (www.tensorflow.org), an open-source library for machine learning. The question Magenta asks is, “Can machines make music and art? If so, how? If not, why not?” The goal if Magenta is to produce open-source tools and models that help creative people be even more creative. I’m primarily looking at how to use so-called “generative” machine learning models to create engaging media. Additionally, I’m working on how to bring other aspects of the creative process into play. For example, art and music is not just about generating new pieces. It’s also about drawing one’s attention, being surprising, telling an interesting story, knowing what’s interesting in a scene, and so on. Before starting the Magenta project, I worked on music search and recommendation for Google Play Music. My research goal in this area was to use machine learning and audio signal processing to help listeners find the music they want when they want it. This involves both learning from audio and learning from how users consume music. In the audio domain, the main goal is to transform the ones and zeros in a digital audio file into something where musically-similar songs are also numerically similar, making it easier to do music recommendation. This is (a) user-dependent: my idea of similar is not the same as yours and (b) changes with context: my idea of similarity changes when I make a playlist for jogging versus making a playlist for a dinner party. I might choose the same song (say "Taxman" by the Beatles) but perhaps it would be the tempo for jogging that drove the selection of that specific song versus "I like the album Revolver and want to add it to the dinner party mix" for a dinner party playlist. I joined Google in 2003. Before then, I was an Associate Professor in Computer Science at University of Montreal. I helped found the BRAMS research center (Brain Music and Sound; www.brams.org) and was involved at the McGill CIRMMT center (Centre for Interdisciplinary Research in Music Media and Technology; www.cirmmt.org). Aside from audio signal processing and machine learning, I worked on music performance modeling. What exactly does a good music performer add to what is already in the score? I treated this as a machine learning question: Hypothetically, if we showed a piano-playing robot a huge collection of Chopin performances--- from the best in the world all the way down to that of a struggling teenage pianist---could it learn to play well by analyzing all of these examples? If so, what’s the right way to perform that analysis? In the end I learned a lot about the complexity and beauty of human music performance, and how performance relates to and extends composition.
Rif A. Saurous (Google)
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