Music is a multimodal art form. While sound plays a key role, other modalities, especially visual, are also critical to enhancing the musical experience. Recently, the MIR field has witnessed a rapid growth of interest in audiovisual processing of music.
This tutorial is intended to introduce this emerging research direction to the broader MIR community. It extends a recently published overview article on audiovisual analysis of music performances  into general audiovisual music processing. Specifically, it provides a comprehensive overview of state-of-the-art research in different aspects of audiovisual music processing, including music performance analysis, content-based retrieval, and music creation. It summarizes datasets, tools and other resources in this field, and articulates challenges and opportunities for future research. An interesting aspect of this tutorial is that it contains two hands-on case studies (30 min each) for the audience to personally experience audiovisual research. Instructions of software environments and starter code will be provided prior to the tutorial for preparation.
To our best knowledge, this is the very first tutorial on audiovisual processing at ISMIR. This tutorial is designed for students and researchers who have general knowledge of music information retrieval and who are interested in learning the state of the art and gaining hands-on experience of audiovisual music processing research. The comprehensive overview and categorization of different aspects of this field will help the audience gain a global view of the research problems, methods, tools, challenges, and opportunities. The hands-on case studies will provide the audience a first-hand experience of the research, helping them quickly arrive at the research frontier. We especially look forward to ideas and inspirations that the MIR community has to offer through this interactive and hands-on tutorial.
 Zhiyao Duan*, Slim Essid*, Cynthia C. S. Liem*, Gaël Richard*, and Gaurav Sharma*, “Audiovisual analysis of music performances: overview of an emerging field,” IEEE Signal Processing Magazine, vol. 36, no. 1, pp. 63-73, 2019. (* authors in alphabetical order)
Github repository: https://github.com/bochen1106/ISMIR2019-Tutorial3-Audiovisual-Music-Proc...
Link to slides on github: https://github.com/bochen1106/ISMIR2019-Tutorial3-Audiovisual-Music-Proc...
Direct download link for slides: https://github.com/bochen1106/ISMIR2019-Tutorial3-Audiovisual-Music-Proc...
Zhiyao Duan is an assistant professor in Electrical and Computer Engineering, Computer Science and Data Science at the University of Rochester. He received his B.S. in Automation and M.S. in Control Science and Engineering from Tsinghua University, China, in 2004 and 2008, respectively, and received his Ph.D. in Computer Science from Northwestern University in 2013. His research interest is in the broad area of computer audition, i.e., designing computational systems that are capable of understanding sounds, including music, speech, and environmental sounds. He is also interested in the connections between computer audition and computer vision, natural language processing, and augmented and virtual reality. He co-presented a tutorial on Automatic Music Transcription at ISMIR 2015. He received a best paper award at the 2017 Sound and Music Computing (SMC) conference, a best paper nomination at the 2017 International Society for Music Information Retrieval (ISMIR) conference, and a CAREER award from the National Science Foundation.
Slim Essid received his state engineering degree from the École Nationale d’Ingénieurs de Tunis, Tunisia, in 2001, his M.Sc. (D.E.A.) degree in digital communication systems from the École Nationale Supérieure des Télécommunications, Paris, France, in 2002, his Ph.D. degree from the Université Pierre et Marie Curie (UPMC), Paris, France, in 2005, and his Habilitation à Diriger des Recherches degree from UPMC in 2015. He is a professor in Telecom ParisTech’s Department of Images, Data, and Signals and the head of the Audio Data Analysis and Signal Processing team. His research interests are machine learning for audio and multimodal data analysis. He has been involved in various collaborative French and European research projects, among them Quaero, Networks of Excellence FP6-Kspace, FP7-3DLife, FP7-REVERIE, and FP-7 LASIE. He has published over 100 peer-reviewed conference and journal papers, with more than 100 distinct coauthors. On a regular basis, he serves as a reviewer for various machine-learning, signal processing, audio, and multimedia conferences and journals, e.g., a number of IEEE transactions, and as an expert for research funding agencies.
Bochen Li received his B.S. from University of Science and Technology of China in 2014. He is currently pursuing a Ph.D. degree in the Department of Electrical and Computer Engineering at the University of Rochester in the USA, under the supervision of Professor Zhiyao Duan. His research interests lie primarily in the inter-disciplinary area of audio signal processing, machine learning, and computer vision towards multimodal analysis of music performances, such as video-informed multipitch estimation and streaming, source separation and association, and expressive performance modeling and generation.
Sanjeel Parekh received B. Tech (hons.) degree in Electronics and Communication engineering from LNM Institute of Information Technology, India in 2014 and M.S. in Sound and Music Computing from Universitat Pompeu Fabra (UPF), Spain in 2015. His Ph.D. thesis titled ‘Learning representations for robust audio-visual scene analysis’ was completed in collaboration with Technicolor R&D and Telecom ParisTech, France between 2016-19. His research focusses on developing and applying machine learning techniques to problems in audio and visual domains. Currently, he is with LTCI lab at Telecom ParisTech, France.