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Titlebook: Computer Music Modeling and Retrieval; Third International Richard Kronland-Martinet,Thierry Voinier,Sølvi Ys Conference proceedings 2006

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书目名称Computer Music Modeling and Retrieval
副标题Third International
编辑Richard Kronland-Martinet,Thierry Voinier,Sølvi Ys
视频videohttp://file.papertrans.cn/234/233629/233629.mp4
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Computer Music Modeling and Retrieval; Third International  Richard Kronland-Martinet,Thierry Voinier,Sølvi Ys Conference proceedings 2006
出版日期Conference proceedings 2006
关键词Performance; Simulation; acoustic signal processing; algorithmic music; cognition; computer music; digital
版次1
doihttps://doi.org/10.1007/11751069
isbn_softcover978-3-540-34027-0
isbn_ebook978-3-540-34028-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2006
The information of publication is updating

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,A Possible Model for Predicting Listeners’ Emotional Engagement,ng decision trees and a set of rules which will be useful for predicting the emotional engagement (EM) experienced by potential listeners in similar pieces. The model is tested on two different movements of the Solos showing very promising results.
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An Interactive Musical Exhibit Based on Infrared Sensors, more in general, non-expert people. Our effort has been focused to carefully design the hardware/software project, in a way that the final user will interact only with non-critical parts of the system.
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Metris: A Game Environment for Music Performance,esign control a musical environment based on just intonation tuning. This presents a scenario where the game design is enhanced by a user’s sense of sound and music. Conventional art music is subverted by responses to simple design elements in a popular game.
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Evaluation of Sensors as Input Devices for Computer Music Interfaces,perceived suitability of these sensors is also examined. Results are given, along with a discussion of their possible implications for computer music interface design and pointers for further work on this topic.
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Improving Prototypical Artist Detection by Penalizing Exorbitant Popularity,t names, e.g. bands whose name equal common speech words (like “Kiss” or “Bush”), we introduce a penalization function. Our approach is demonstrated on a data set containing 224 artists from 14 genres.
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