scholarly journals An Interactive Garment for Orchestra Conducting: IoT-enabled Textile & Machine Learning to Direct Musical Performance

2021 ◽  
Author(s):  
Berit Greinke ◽  
Giorgia Petri ◽  
Pauline Vierne ◽  
Paul Biessmann ◽  
Alexandra Börner ◽  
...  
Keyword(s):  
Machine Learning ◽  
Musical Performance ◽  
Textile Machine

scholarly journals Intention, Effort, and Restraint: The EMG in Musical Performance

Leonardo ◽  
2015 ◽  
Vol 48 (3) ◽  
pp. 298-299 ◽  
Author(s):  
Atau Tanaka
Keyword(s):  
Machine Learning ◽  
Muscle Activity ◽  
Multimodal Interaction ◽  
Musical Instrument ◽  
Musical Performance ◽  
Instrument Response ◽  
Challenges And Opportunities

The author presents the challenges and opportunities in the use of the electromyogram (EMG), a signal representing muscle activity, for digital musical instrument applications. The author presents basic mapping paradigms and the place of the EMG in multimodal interaction and describes initial trials in machine learning. It is proposed that nonlinearities in musical instrument response cannot be modelled only by parameter interpolation and require strategies of extrapolation. The author introduces the concepts of intention, effort, and restraint as such strategies, to exploit, as well as confront limitations of, the use of muscle signals in musical performance.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Mathematics for Machine Learning

2020 ◽  
Author(s):  
Marc Peter Deisenroth ◽  
A. Aldo Faisal ◽  
Cheng Soon Ong
Keyword(s):  
Machine Learning

Phase Transitions in Machine Learning

2009 ◽  
Author(s):  
Lorenza Saitta ◽  
Attilio Giordana ◽  
Antoine Cornuejols
Keyword(s):  
Machine Learning ◽  
Phase Transitions

Understanding Machine Learning

2009 ◽  
Author(s):  
Shai Shalev-Shwartz ◽  
Shai Ben-David
Keyword(s):  
Machine Learning
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