Indonesian Summer School on Music Information Retrieval
14 – 18 August 2017 – Universitas Indonesia, Depok (Jakarta, Indonesia) –
The Indonesian Summer School was the first summer school in Music Information Retrieval in Indonesia. The summer school covered basic knowledge in Information Retrieval, music content analysis, content based music indexing, music classification, automatic feature learning, user studies, and music recommendation. The summer school program also included some Lab sessions.
The summer school was intended for 4th year undergraduate students, master and PhD students, researchers in institutions and industry. It was intended to give the participants some backgrounds to work on music information retrieval.
The Summer School was organized by Universitas Indonesia, Vienna University of Technology (TU Wien, Austria) and Johannes Kepler University (Linz, Austria) and was supported by the ASEA-UNINET.
– Bruce Ferwerda: Assistant Professor in the Computer Science and Informatics department of the School of Engineering at Jönköping University, Sweden and currently also a researcher in the Department of Computational Perception at the Johannes Kepler University in Linz, Austria. He received his master’s degree in Human-Technology Interaction from Eindhoven University of Technology in the Netherlands and received his PhD degree in Technical Sciences from the Johannes Kepler University in Austria.
– Markus Schedl: Associate Professor at the Johannes Kepler University Linz / Department of Computational Perception. He graduated in Computer Science from the Vienna University of Technology and earned his Ph.D. in Computer Science from the Johannes Kepler University Linz.
– Mirna Adriani: Currently she is the head of the Information Retrieval Lab in the Faculty of Computer Science, Universitas Indonesia. Her PhD in Computer Science she received from the Glasgow University in Scotland UK.
– Peter Knees: Assistant Professor of the Faculty of Informatics, Institute of Software Technology and Interactive Systems of the Vienna University of Technology. He holds a Master’s degree in Computer Science from the Vienna University of Technology and a Ph.D. degree from the Johannes Kepler University Linz.
– Thomas Lidy: Currently he is the Head of Machine Learning at Musimap, a startup providing a large-scale human+AI based music recommender and search engine. He has a long-standing experience in audio analysis and Music Information Retrieval, which he has gathered in more than 12 years of working in this domain as a researcher at the Vienna University of Technology in an international context. In 2008, Thomas had founded Spectralmind, an innovative music technology company that created both professional music search products and mobile apps for visual music discovery. His master degree in Informatics he received from the Vienna University of Technology.
– Yohanes Stefanus: Lektor Kepala (Associate Professor) at the Faculty of Computer Science, Universitas Indonesia. His research expertise is in Computer-Aided Geometric Design, Wavelet-Transform-based Techniques, and Computational Logic. He also works on Machine Learning and Symbolic Computation. He holds a M.Math. degree and a Ph.D. degree in Computer Science from the University of Waterloo, Canada.
Professors and researchers from ASEA UNINET have been invited to participate, together with their students, in this summer school via live video lecturing.
Links for the video streaming via Youtube, 14 – 18 August 2017, have been the following:
Day 2 – August 15: https://youtu.be/pItzrAsCM9s
Day 3 – August 16: https://www.youtube.com/watch?v=QoYzj-JaVrw
Day 4 – August 18: https://www.youtube.com/watch?v=t414HMiuu0I
- Monday, 14 August 2017
09.00 – 10.30 Intro to MIR (Knees)
10.45 – 12.15 Music content analysis (Knees)
13.30 – 15.00 Basics in information retrieval and machine learning (Adriani)
15.30 – 16.00 Introduction to Labs (Knees, Lidy)
- Tuesday, 15 August 2017
09.00 – 10.30 Music content similarity, genre/mood classification (Schedl)
10.45 – 12.15 Hiding and Retrieving Information in Music using Wavelet Transform (Stefanus)
13.30 – 15.00 Lab: music content similarity, genre/mood classification (Knees, Lidy)
- Wednesday, 16 August 2017
09.00 – 10.30 Deep learning / automatic feature learning (Lidy)
10.45 – 12.15 Lab: deep learning / automatic feature learning (Lidy)
13.30 – 15.00 Music context-based similarity and indexing
- Thursday, 17 August 2017
No classes due to Independence Day
- Friday, 18 August 2017
09.00 – 10.30 Lab: music context-based similarity and indexing (Schedl)
10.45 – 11.30 Designing for users 1 (Ferwerda)
13.30 – 15.00 Designing for users 2 (Ferwerda)
The campus of Universitas Indonesia offered a beautiful environment to study and was appreciated by the participants.