ISMIR 2019: Difference between revisions
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|End date=2019/11/08 | |End date=2019/11/08 | ||
|Homepage=https://ismir2019.ewi.tudelft.nl/ | |Homepage=https://ismir2019.ewi.tudelft.nl/ | ||
|Twitter account=@ismir2019 | |||
|City=Delft | |City=Delft | ||
|Country=Netherlands | |Country=Netherlands | ||
|Has host organization=TU Delft | |Has host organization=TU Delft | ||
|has general chair=Cynthia C. S. Liem, Emilia Gómez | |||
|has program chair=Arthur Flexer, Geoffroy Peeters, Julián Urbano, Anja Volk | |||
|has Proceedings Link=https://dblp.org/db/conf/ismir/ismir2019.html | |||
}} | }} | ||
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. | |||
Latest revision as of 17:50, 4 June 2020
| 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.