Difference between revisions of "ISMIR 2019"
Jump to navigation
Jump to search
Heike.Rohde (talk | contribs) |
Tim Holzheim (talk | contribs) (Added page provenance(#264) and contribution type(#271)) |
||
Line 14: | Line 14: | ||
|has program chair=Arthur Flexer, Geoffroy Peeters, Julián Urbano, Anja Volk | |has program chair=Arthur Flexer, Geoffroy Peeters, Julián Urbano, Anja Volk | ||
|has Proceedings Link=https://dblp.org/db/conf/ismir/ismir2019.html | |has Proceedings Link=https://dblp.org/db/conf/ismir/ismir2019.html | ||
+ | |pageCreator=Heike.Rohde | ||
+ | |pageEditor=Heike.Rohde | ||
+ | |contributionType=1 | ||
}} | }} | ||
Topics of Interest | Topics of Interest |
Latest revision as of 19:57, 1 April 2022
ISMIR 2019 | |
---|---|
20t20th International Society for Music Information Retrieval Conference
| |
Event in series | ISMIR |
Dates | 2019/11/04 (iCal) - 2019/11/08 |
Homepage: | https://ismir2019.ewi.tudelft.nl/ |
Twitter account: | @ismir2019 |
Location | |
Location: | Delft, Netherlands |
Committees | |
General chairs: | Cynthia C. S. Liem, Emilia Gómez |
PC chairs: | Arthur Flexer, Geoffroy Peeters, Julián Urbano, Anja Volk |
Table of Contents | |
Tweets by @ismir2019 | |
Topics of Interest
* MIR data and fundamentals: music signal processing; symbolic music processing; metadata, tags, linked data, and semantic web; lyrics and other textual data, web mining, and natural language processing; multimodality. * Domain knowledge: representations of music; music acoustics; computational music theory and musicology; cognitive MIR; machine learning/artificial intelligence for music. * Evaluation and Methodology: philosophical and methodological foundations; evaluation methodology and reproducibility; statistical methods for evaluation; MIR tasks, datasets and annotation protocols; evaluation metrics. * Musical features and properties: melody and motives; harmony, chords and tonality; rhythm, beat, tempo; structure, segmentation and form; timbre, instrumentation and voice; musical style and genre; musical affect, emotion and mood; expression and performative aspects of music. * Music processing: sound source separation; music transcription and annotation; optical music recognition; alignment, synchronization and score following; music summarization; music synthesis and transformation; fingerprinting; automatic classification; indexing and querying; pattern matching and detection; similarity metrics. * User-centered MIR: user behavior and modeling; human-computer interaction and interfaces; personalization; user-centered evaluation; legal, social and ethical issues. * Applications: digital libraries and archives; music retrieval systems; music recommendation and playlist generation; music and health, well-being and therapy; music training and education; music composition, performance and production; gaming; business and marketing.