![]() in Musicology from the Aristotle University of Thessaloniki. in Composition from Paris Vincennes University, and an integrated M.A. ![]() degree in Mathematical Logic from Paris Diderot University, an M.A. ![]() His research topics encompass graph neural networks, music structure segmentation, and automated music analysis. student at the Institute of Computational Perception, Johannes Kepler University, Linz, Austria. His research interests include post-hoc explainability techniques for DL models, grammar-based parsing of hierarchical chord structures, piano comping generation for jazz music, and voice separation in symbolic music.Įmmanouil Karystinaios is a Ph.D. at CNAM Paris on music transcription, with a focus on the production of musical scores, and holds classical and jazz piano degrees from the Conservatory of Vicenza. degree in Electrical Engineering and Audio Engineering from the Graz University of Technology, a degree in Physics from the National Autonomous University of Mexico, and a degree in Piano Performance from the National Conservatory of Music of Mexico.įrancesco Foscarin is a postdoctoral researcher at the Institute of Computational Perception, Johannes Kepler University, Linz, Austria. He received a doctoral degree in Computer Science at the Institute of Computational Perception of the Johannes Kepler University Linz, a M.Sc. His research focuses on studying expressive music performance, music cognition, and music theory with machine learning methods. This work receives funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme, grant agreement No 101019375 (Whither Music?).Ĭarlos Cancino-Chacón is an Assistant Professor at the Institute of Computational Perception, Johannes Kepler University, Linz, Austria, and a Guest Researcher at the RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Norway. Furthermore, some familiarity with the basic concepts of statistics and machine learning is useful. For the hands-on parts of the tutorial, we presuppose some practical experience with the Python language, but we will provide well-documented step-by-step access to the code in the form of Google Colab notebooks, which will be made publicly available after the tutorial. Therefore, we target a broad audience of researchers without requiring prior knowledge of this particular area. The motivation behind this tutorial is to promote research on symbolic music processing in the MIR community. The second, third, and fourth parts are hands-on tutorials that showcase the structure of the Partitura package (including its relation to other popular Python packages for symbolic music processing), how to extract common MIR features, and how to work with symbolic multimodal datasets, respectively. The tutorial will be structured in four parts: The first part provides an introduction to the topic of symbolic music processing. To target different kinds of symbolic data, we use an extended version of the ASAP Dataset, a multi-modal dataset that contains MusicXML scores, MIDI performances, audio performances, and score-to-performance alignments. We do this with the aid of the Python package Partitura. This tutorial aims to provide an introduction to symbolic music processing for a broad MIR audience, with a particular focus on showing how to extract relevant MIR features from symbolic musical formats in a fast, intuitive, and scalable way. Such data can be used as both input/training data and as ground truth for MIR systems. Symbolic music formats (e.g., MIDI, MusicXML/MEI) can provide a variety of high-level musical information like note pitch and duration, key/time signature, beat/downbeat position, etc. T1(M): An Introduction to Symbolic Music Processing in Python with PartituraĬarlos Cancino-Chacón, Francesco Foscarin, Emmanouil Karystinaios, Silvan David Peter There will be three parallel tutorials each in the morning and the afternoon sessions, with a total of six tutorials. All the tutorials at ISMIR 2022 will take place on 04 December, 2022 in a hybrid format.
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