Difference between revisions of "ISMIR 2019"
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Topics of Interest | Topics of Interest | ||
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| − | Domain knowledge: representations of music; music acoustics; computational music theory and musicology; cognitive MIR; machine learning/artificial intelligence for music. | + | * 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. |
| − | Evaluation and Methodology: philosophical and methodological foundations; evaluation methodology and reproducibility; statistical methods for evaluation; MIR tasks, datasets and annotation protocols; evaluation metrics. | + | * Domain knowledge: representations of music; music acoustics; computational music theory and musicology; cognitive MIR; machine learning/artificial intelligence for music. |
| − | 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. | + | * Evaluation and Methodology: philosophical and methodological foundations; evaluation methodology and reproducibility; statistical methods for evaluation; MIR tasks, datasets and annotation protocols; evaluation metrics. |
| − | 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. | + | * 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. |
| − | User-centered MIR: user behavior and modeling; human-computer interaction and interfaces; personalization; user-centered evaluation; legal, social and ethical issues. | + | * 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. |
| − | 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. | + | * 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. | ||
Revision as of 19:21, 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.