We’re thrilled to share that our team from @cpjku has brought home three awards from the @DCASE_Workshop in Tokyo! 🏆
- Best Student Paper Award
- Judges' Award for DCASE Challenge Task 4
- Judges' Award for DCASE Challenge Task 8
#DCASE2024#MIR
This article by Francesco Foscarin is for people planning or beginning to work with MIDI files. This format is widely used in the music community, and it caught the attention of computer music researchers due to the availability of datasets.
towardsdatascience.com/midi-files-as-……
The Ars Electronica 2024 officially started today🔥!
Our institute is participating with 2 exciting projects:
- Hack the Hat: Mastering the Recruitment AI Gatekeeper
- Raise your Voice: raiseyourvoice.cp.jku.at
Check them out @ JKU MED Campus
More info: jku.at/aefestival
[AS] Roman Numeral Analysis with Graph Neural Networks: Onset-wise Predictions from Note-wise Features
E Karystinaios, G Widmer [Johannes Kepler University Linz] (2023)
arxiv.org/abs/2307.03544
🚀 Excited to present my paper, "Perception-Inspired Graph Convolution for Music Understanding Tasks," at @IJCAIconf 2024 in Jeju, South Korea next week!
Thanks to my co-author @fosfrancesco for his contributions.
Read our paper here: arxiv.org/abs/2405.09224#IJCAI2024#MIR
🚀 Partitura release v1.5.0 is out! 🎉 Enjoy new features, better performance, and support for MusicXML, MIDI, Kern, and MEI import/export. Details: [Release Notes](github.com/CPJKU/partitur…)
Try the Partitura python package for symbolic music processing now!
#SymbolicMusic#MIR
Our highlights this week include three top-notch articles on computer vision and object detection, by Lihi Gur Arie, Raul Vizcarra Chirinos, and @patrick_llgc, plus recommended reads on other topics by authors like @anand_subu, @manoskary, and @ArtFishAI. buff.ly/3S3oDwF
In a fascinating new post at the intersection of machine learning and music analysis, Emmanouil Karystinaios presents MusGConv, a perception-based graph convolution block for symbolic music applications. buff.ly/3xMMObP
🎉 Excited to present our research on SMUG-Explain at the Sound and Music Computing Conference this week! 🎶 This framework makes GNNs in music more interpretable by visualizing explanations directly in scores. #SMC2024#xAI#GNNs
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