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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.
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