01.09.2024
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@xxxxxxxxxxxxxx.
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@xxxxxxxxxxxxxx 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