we are happy to announce the Call for Papers for TISMIR Special
Collection on Multi-Modal Music Information Retrieval.
*Deadline for Submissions
*01.08.2024*
*
*Scope of the Special Collection*
Data related to and associated with music can be retrieved from a
variety of sources or modalities:
audio tracks; digital scores; lyrics; video clips and concert
recordings; artist photos and album covers;
expert annotations and reviews; listener social tags from the Internet;
and so on. Essentially, the ways
humans deal with music are very diverse: we listen to it, read reviews,
ask friends for
recommendations, enjoy visual performances during concerts, dance and
perform rituals, play
musical instruments, or rearrange scores.
As such, it is hardly surprising that we have discovered multi-modal
data to be so effective in a range
of technical tasks that model human experience and expertise. Former
studies have already
confirmed that music classification scenarios may significantly benefit
when several modalities are
taken into account. Other works focused on cross-modal analysis, e.g.,
generating a missing modality
from existing ones or aligning the information between different modalities.
The current upswing of disruptive artificial intelligence technologies,
deep learning, and big data
analytics is quickly changing the world we are living in, and inevitably
impacts MIR research as well.
Facilitating the ability to learn from very diverse data sources by
means of these powerful approaches
may not only bring the solutions to related applications to new levels
of quality, robustness, and
efficiency, but will also help to demonstrate and enhance the breadth
and interconnected nature of
music science research and the understanding of relationships between
different kinds of musical
data.
In this special collection, we invite papers on multi-modal systems in
all their diversity. We particularly
encourage under-explored repertoire, new connections between fields, and
novel research areas.
Contributions consisting of pure algorithmic improvements, empirical
studies, theoretical discussions,
surveys, guidelines for future research, and introductions of new data
sets are all welcome, as the
special collection will not only address multi-modal MIR, but also cover
multi-perspective ideas,
developments, and opinions from diverse scientific communities.
*Sample Possible Topics*
● State-of-the-art music classification or regression systems which are
based on several
modalities
● Deeper analysis of correlation between distinct modalities and
features derived from them
● Presentation of new multi-modal data sets, including the possibility
of formal analysis and
theoretical discussion of practices for constructing better data sets in
future
● Cross-modal analysis, e.g., with the goal of predicting a modality
from another one
● Creative and generative AI systems which produce multiple modalities
● Explicit analysis of individual drawbacks and advantages of modalities
for specific MIR tasks
● Approaches for training set selection and augmentation techniques for
multi-modal classifier
systems
● Applying transfer learning, large language models, and neural
architecture search to
multi-modal contexts
● Multi-modal perception, cognition, or neuroscience research
● Multi-objective evaluation of multi-modal MIR systems, e.g., not only
focusing on the quality,
but also on robustness, interpretability, or reduction of the
environmental impact during the
training of deep neural networks
*Guest Editors*
● Igor Vatolkin (lead) - Akademischer Rat (Assistant Professor) at the
Department of Computer
Science, RWTH Aachen University, Germany
● Mark Gotham - Assistant professor at the Department of Computer
Science, Durham
University, UK
● Xiao Hu - Associated professor at the University of Hong Kong
● Cory McKay - Professor of music and humanities at Marianopolis
College, Canada
● Rui Pedro Paiva - Professor at the Department of Informatics
Engineering of the University of
Coimbra, Portugal
*Submission Guidelines*
Please, submit through https://transactions.ismir.net, and note in your
cover letter that your paper is
intended to be part of this Special Collection on Multi-Modal MIR.
Submissions should adhere to formatting guidelines of the TISMIR journal:
https://transactions.ismir.net/about/submissions/. Specifically,
articles must not be longer than
8,000 words in length, including referencing, citation and notes.
Please also note that if the paper extends or combines the authors'
previously published research, it
is expected that there is a significant novel contribution in the
submission (as a rule of thumb, we
would expect at least 50% of the underlying work - the ideas, concepts,
methods, results, analysis and
discussion - to be new).
In case you are considering submitting to this special issue, it would
greatly help our planning if you
let us know by replying to igor.vatolkin@rwth-aachen.de.
Kind regards,
Igor Vatolkin
on behalf of the TISMIR editorial board and the guest editors
--
Dr. Igor Vatolkin
Akademischer Rat
Department of Computer Science
Chair for AI Methodology (AIM)
RWTH Aachen University
Theaterstrasse 35-39, 52062 Aachen
Mail:igor.vatolkin@rwth-aachen.de
Skype: igor.vatolkin
https://www.aim.rwth-aachen.de
https://sig-ma.de
https://de.linkedin.com/in/igor-vatolkin-881aa78
https://scholar.google.de/citations?user=p3LkVhcAAAAJ
https://ls11-www.cs.tu-dortmund.de/staff/vatolkin
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