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https://ceur-ws.org/Vol-2068/preface-milc.pdf
IUI’18 Workshop on Intelligent Music Interfaces for
Listening and Creation (MILC)
Peter Knees Markus Schedl Rebecca Fiebrink
TU Wien Johannes Kepler University Goldsmiths, University of
Vienna, Austria Linz, Austria London, United Kingdom
peter.knees@tuwien.ac.at markus.schedl@jku.at r.fiebrink@gold.ac.uk
ABSTRACT with adaptive user interface experts and provides a forum for
Digital music technology constitutes a key factor in the music the latest developments and trends in intelligent interfaces in
ecosystem. Through intelligent user interfaces, music con- these areas.1
sumers and producers can effectively and intuitively access
and create sound. The goal of the MILC workshop is to CONTRIBUTIONS
provide a forum for the latest developments and trends in intel- The contributions to the MILC workshop reflect the relevance
ligent interfaces for music listening and creation by bringing of intelligent interfaces on both ends of the spectrum.
together researchers from areas such as interactive machine Papers dealing with personalization address needs of both, con-
learning, music information retrieval, recommender systems, sumers and creators. In “How Automated Recommendations
human computer interaction, and adaptive systems. Affect the Playlist Creation Behavior of Users,” Kamehkhosh
et al. analyze the influence playlist construction support tools
ACM Classification Keywords
have on resulting playlists and user behavior. In “geMsearch:
H.5.2 Information Interfaces and Presentation: User Interfaces; Personalized Explorative Music Search,” Esswein et al. make
H.5.5 Information Interfaces and Presentation: Sound and music collections accessible by facilitating approximate query-
Music Computing ing and visualization through low-dimensional vector represen-
tations learned via graph embedding. Shi and Mysore propose
Author Keywords
“MedleyAssistant – A system for personalized music medley cre-
music listening; music creation; sound synthesis; music ation” that enables also non-experts to create medleys, while
recommendation; music information retrieval maintaining the possibility to express their individual style.
MOTIVATION Interaction with intelligent music systems and user interfaces
Today’s music ecosystem is permeated by digital technology – presents another diverse area. Vigliensoni et al. propose an
from recording to production to distribution to consumption. interactive machine learning approach to optical music recog-
Intelligent technologies and interfaces play a crucial role dur- nition in “An environment for machine pedagogy: Learning
ing all these steps. On the creation side, tools and interfaces how to teach computers to read music” and show that perfor-
like new sensor-based musical instruments or software like mance is continuously improved when humans can intervene
digital audio workstations and sound and sample browsers and correct, therefore teach, the machine. Lindh questions
support creativity. Generative systems can support novice and investigates usability, accessibility and intuitiveness of the
and professional musicians by automatically synthesizing new ubiquitous skeuomorphic design in music creation interfaces
sounds or even new musical material. On the consumption in “Beyond a Skeuomorphic Representation of Subtractive
side, tools and interfaces such as recommender systems, auto- Synthesis”. In “Overviewing a Field of Self-Organising Music
matic radio stations, or active listening applications allow users Interfaces: Autonomous, Distributed, Environmentally Aware,
to navigate the virtually endless spaces of music repositories. Feedback Systems,” Kollias identifies and surveys the area of
“self-organising music,” which denotes a field comprising of
Both ends of the music market therefore heavily rely on and various intelligent sound and music interfaces and systems.
benefit from intelligent approaches that enable users to ac-
cess sound and music in unprecedented manners. This on- Intelligent approaches to composition support music creators
going trend draws from manifold areas such as interactive and open up new perspectives. Roberts et al. introduce an
machine learning, music information retrieval (MIR) – in par- interface to explore complex note sequence, drum pattern, and
ticular content-based retrieval systems, recommender systems, timbre spaces with intuitive controls by utilizing deep-learned
human-computer interaction, and adaptive systems, to name representations in “Learning Latent Representations of Music
but a few prominent examples. In this light, the MILC work- to Generate Interactive Musical Palettes”. In “Lumanote:
shop held in the context of IUI, fosters the convention of the A Real-Time Interactive Music Composition Assistant” by
digital music creation and performance and MIR communities Granger et al., songwriters are interactively supported with
real-time, scale-aware chord and note suggestions in the pro-
©2018. Copyright for the individual papers remains with the authors. cess of composition.
Copying permitted for private and academic purposes. 1 https://iui2018milc.github.io
MILC ’18, March 11, 2018, Tokyo, Japan
WORKSHOP ORGANIZERS PROGRAM COMMITTEE
Peter Knees is an Assistant Professor of the Institute of • Baptiste Caramiaux, IRCAM, France
Information Systems Engineering of TU Wien. In the last • Mark Cartwright, New York University, USA
decade, he has been an active member of the Music Informa-
• Matthew Davies, INESC TEC Porto, Portugal
tion Retrieval research community, reaching out to the related
areas of multimedia, text IR, and recommender systems. • Christian Dittmar, International Audio Laboratories Erlan-
Webpage: https://www.ifs.tuwien.ac.at/~knees/ gen, Germany
• Bruce Ferwerda, Jönköping University, Sweden
Markus Schedl is an associate professor at the Department • Ichiro Fujinaga, McGill University, Canada
of Computational Perception of the Johannes Kepler Univer-
• Jason Hockman, Birmingham City University, UK
sity Linz. His main research interests include web and social
media mining, recommender systems, information retrieval, • Masataka Goto, National Institute of Advanced Industrial
multimedia, and music information research. He (co-)authored Science and Technology, Japan
more than 150 refereed conference papers and journal articles. • Florian Grote, Native Instruments GmbH, Germany
Webpage: http://www.cp.jku.at/people/schedl/ • Bogdan Ionescu, University Politehnica of Bucharest, Ro-
mania
Rebecca Fiebrink is a Senior Lecturer at Goldsmiths, Uni-
• Vikas Kumar, University of Minnesota, USA
versity of London. Much of her research focuses on designing
the use of machine learning as a creative tool. Fiebrink is the • Cynthia Liem, Delft University of Technology, Netherlands
developer of the Wekinator, open-source software for real-time • Matija Marolt, University of Ljubljana, Slovenia
interactive machine learning whose current version has been • Cárthach Ó Nuanáin, Universitat Pompeu Fabra, Spain
downloaded over 10,000 times. She is the creator of a MOOC
titled “Machine Learning for Artists and Musicians,” which • Tae Hong Park, New York University, USA
launched in 2016 on the Kadenze platform. • Sebastian Stober, University of Potsdam, Germany
Webpage: https://www.doc.gold.ac.uk/~mas01rf/ • Michael Zbyszynski, Goldsmiths University of London, UK