Titlebook: Bridging People and Sound; 12th International S Mitsuko Aramaki,Richard Kronland-Martinet,Sølvi Ys Conference proceedings 2017 Springer Int

TOXIN

Lecture Notes in Computer Sciencehttp://image.papertrans.cn/b/image/190766.jpg

NAVEN

Bridging People and Sound978-3-319-67738-5Series ISSN 0302-9743 Series E-ISSN 1611-3349

Myofibrils

https://doi.org/10.1007/978-3-658-12311-6nt framework of “Copista”, an OMR system proposed to recognize handwritten scores especially regarding a historical music collection. “Copista” is the Brazilian word for Scribe, someone who writes music scores. The proposed system is useful to music collection preservation and supporting further research and development of OMR systems.

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弄脏

Eclipse: A Wearable Instrument for Performance Based StorytellingEclipse is a dance performance telling the Altai Shaman story of a solar eclipse. Through movement of a dancer, a unique soundscape is created, to which the dancer moves. This feedback mechanism tries to achieve a multisensory interaction where the sounds created by movements are supplementary to the visual perception of the movements.

倒转

deepGTTM-I&II: Local Boundary and Metrical Structure Analyzer Based on Deep Learning Techniquerning analyzer called deepGTM-I&II based on the above deep learning technique to learn the relationship between scores and metrical structures in the following three steps. First, we conduct unsupervised pre-training of a network using 15,000 pieces of music in a non-labeled dataset. After pre-train

有杂色

User Experience in an Interactive Music Virtual Reality System: An Exploratory Studyesign criteria and discussion of implications for facilitating music engagement in virtual reality. Finally our work discusses the effectiveness of measures with respect to future evaluation of novel interactive music systems in virtual reality.

种子

Beratungstechnologien zur Implementierung,rning analyzer called deepGTM-I&II based on the above deep learning technique to learn the relationship between scores and metrical structures in the following three steps. First, we conduct unsupervised pre-training of a network using 15,000 pieces of music in a non-labeled dataset. After pre-train

coagulate

ACME

Mitsuko Aramaki,Richard Kronland-Martinet,Sølvi Ys