Submissions are invited for a new journal in the area of connected speech. To submit articles please go to https://journal.equinoxpub.com/JCS/about/submissions

The aim of the Journal of Connected Speech is to provide a platform for the study of connected speech in both its formal and functional aspects (from prosody to discourse analysis). The journal explores issues linked to transcription systems, instrumentation, and data collection methodology, as well as models within broadly functional, cognitive, and psycholinguistic approaches.

The journal launches in 2024. See https://journal.equinoxpub.com/index.php/JCS/index

If you have any queries, please contact me at m.j.ball@bangor.ac.uk

Martin J. Ball, DLitt, PhD, HonFRCSLT, FLSW

Athro er Anrhydedd,

Ysgol Iaith, Diwylliant a'r Celfyddydau,

Prifysgol Bangor, Cymru.

(Hefyd Athro Gwadd, Prifysgol Glynd?r Wrecsam)

Honorary Professor,

School of Arts, Culture and Language,

Bangor University, Wales.

(Also Visiting Professor, Wrexham Glynd?r University)

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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

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