International Journal of Arts and Technology
- Editor in Chief
- Prof. Athanasios Vasilakos
- ISSN online
- ISSN print
- 4 issues per year
- CiteScore 2020 0.6
IJART addresses arts and new technologies, highlighting computational art. With evolution of intelligent devices, sensors and ambient intelligent/ubiquitous systems, projects are exploring the design of intelligent artistic artefacts. Ambient intelligence supports the vision that technology becomes invisible, embedded in our natural surroundings, present whenever needed, attuned to all senses, adaptive to users/context and autonomously acting, bringing art to ordinary people, offering artists creative tools to extend the grammar of the traditional arts. Information environments will be the major drivers of culture.
Topics covered include
- New media arts, science and technology
- Interactive/visual theatre, neurobiological base of acting, digital/wearable cinema
- Augmented performance in dance
- Artificial intelligence-based art practice, web art and postmodernism
- Using analysis of artworks in conjunction with AmI to produce novel objects
- Using AmI to promote the creativity of a human user
- Autonomic sensor networks and wearable computers in the performing arts
- Computer vision and optical tracking for music and dance performance
- Cognitive intelligence and natural intelligence for the arts
- Collaborative distributed environments
- Evolutionary art systems that create drawings/images/animations/sculptures/poetry/text
- Evolutionary music systems that create musical pieces/sounds/instruments/voices
- Choreographing media for interactive virtual environments
- New media actors, new media aesthetics
- Social and ethical issues in the arts and technology
The objectives of IJART are to address new works, research and performances in the multi-disciplinary emerging area of new technologies and the arts - and to provide a common platform under which this artwork can be published and disseminated. IJART provides a high-quality platform for this purpose.
IJART provides a vehicle to help professionals, academics, researchers ,artists, museum curators, and graduate students working in the field of arts and technology, to disseminate information and to learn from each other's work.
IJART publishes original research papers, review papers, artworks, performances, conference reports, book reviews, notes, commentaries, and news. Special Issues devoted to important topics in the arts and new technologies will occasionally be published.
IJART is indexed in:
- Scopus (Elsevier)
- Compendex [formerly Ei] (Elsevier)
- Emerging Sources Citation Index (Clarivate Analytics)
- Academic OneFile (Gale)
- cnpLINKer (CNPIEC)
IJART is listed in:More journal lists/directories...
Editor in Chief
- Vasilakos, Athanasios, Lulea University of Technology, Sweden
- Wan, Jiafu, South China University of Technology, China
- Xia, Zhihua, Nanjing University of Information Science and Technology, China
Editorial Board Members
- Brooks, Tony, Aalborg University, Denmark
- Chen, Min, Seoul National University, South Korea
- Cheok, Adrian David, National University of Singapore, Singapore
- Draisin, Maya, International Academy of Digital Arts & Sciences (IADAS), USA
- El-Nasr, Magy Seif, Simon Fraser University, Canada
- Fisher, Scott S., University of Southern California, Los Angeles, USA
- Fishwick, Paul, University of Florida, USA
- Grau, Oliver, Danube University, Austria
- Gross, Tom, University of Bamberg, Germany
- Hu, Jun, Eindhoven University of Technology, Netherlands
- Inakage, Masa, Keio University, Japan
- Ishii, Hiroshi, MIT Media Laboratory, USA
- Kato, Hirokazu, Nara Institute of Science and Technology, Japan
- Lee, Newton, Institute for Education, Research, and Scholarships, USA
- Maes, Patti, MIT Media Laboratory, USA
- Marranca, Bonnie, PAJ Publications, USA
- Natkin, Stéphane, Conservatoire National des Arts et Métiers, France
- Pan, Zhigeng, Zhejiang University, China
- Pentland, Alex (Sandy), MIT Media Laboratory, USA
- Rokem, Freddie, Tel Aviv University, Israel
- Salem, Ben, Kwansei Gakuin University, Japan
- Sarfraz, Muhammad, Kuwait University, Kuwait
- Selker, Ted, MIT Media Laboratory, USA
- Shu, Lei, Guangdong University of Petrochemical Technology, China
- Vercoe, Barry, MIT Media Laboratory, USA
- Wilson, Stephen, San Francisco State University, USA
- Xiong, Neal Naixue, Colorado Technical University, USA
A few essentials for publishing in this journal
- Submitted articles should not have been previously published or be currently under consideration for publication elsewhere.
- Conference papers may only be submitted if the paper has been completely re-written (more details available here) and the author has cleared any necessary permissions with the copyright owner if it has been previously copyrighted.
- Briefs and research notes are not published in this journal.
- All our articles go through a double-blind review process.
- All authors must declare they have read and agreed to the content of the submitted article. A full statement of our Ethical Guidelines for Authors (PDF) is available.
- There are no charges for publishing with Inderscience, unless you require your article to be Open Access (OA). You can find more information on OA here.
- All articles for this journal must be submitted using our online submissions system.
- View Author guidelines.
Optical music recognition
23 June, 2021
Optical character recognition (OCR) commonly used to convert the text in scanned documents into a searchable and editable form on the computer is a well-established digitisation technique. But, what about other kinds of documents, rich with meaning, such as musical manuscripts? New work in the International Journal of Arts and Technology discusses the possibility of optical musical recognition, OMR. A new approach developed by a team at Bina Nusantara University in Jakarta, Indonesia, uses deep machine learning and a convolutional neural network trained to recognise the nuance of musical notation on known manuscripts. The algorithm can then convert a newly presented musical manuscript into a digitized form with 8 percent accuracy [...]More details...