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.

Challenges and Opportunities for Data Science and Machine Learning in IoT Systems - A Timely Debate: Part 2

2020 ◽  
pp. 2-6
Author(s):  
Sumi Helal ◽  
Flavia C. Delicato ◽  
Cintia B. Margi ◽  
Satyajayant Misra ◽  
Markus Endler
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Challenges And Opportunities

scholarly journals Brain Musics: History, Precedents, and Commentary on Whalley, Mavros and Furniss

2015 ◽  
Vol 9 (3-4) ◽  
pp. 277 ◽  
Author(s):  
Miguel Ortiz ◽  
Mick Grierson ◽  
Atau Tanaka
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Musical Performance ◽  
Eeg Signals ◽  
Live Electronics ◽  
Broad Area ◽  
Direct Mapping ◽  
Challenges And Opportunities ◽  
History Of ◽  
Small Ensemble

<p>Whalley, Mavros and Furniss (this issue) explore questions of agency, control and interaction, as well as the embodied nature of musical performance in relation to the use of human-computer interaction through the work <em>Clasp Together (beta) </em>for small ensemble and live electronics. The underlying concept of the piece focuses on direct mapping of a human neural network (embodied by a performer within the ensemble) to an artificial neural network running on a computer. With our commentary, we contextualize the work by offering a brief history of music that uses brainwaves. We review the use of EEG signals for musical performance and point at precedents in EEG-based musical practice. We hope to more clearly situate <em>Clasp Together (beta)</em> in the broad area of Brain Computer Musical Interfaces and discuss the challenges and opportunities that these technologies offer for composers.</p>

scholarly journals Machine learning for energy-water nexus: challenges and opportunities

2018 ◽  
Vol 2 (3) ◽  
pp. 228-267 ◽  
Author(s):  
Zaidi ◽  
Chandola ◽  
Allen ◽  
Sanyal ◽  
Stewart ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Systems ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Technological Advancement ◽  
Research Directions ◽  
Learning Techniques ◽  
Challenges And Opportunities ◽  
Future Research Directions ◽  
System Sustainability

Modeling the interactions of water and energy systems is important to the enforcement of infrastructure security and system sustainability. To this end, recent technological advancement has allowed the production of large volumes of data associated with functioning of these sectors. We are beginning to see that statistical and machine learning techniques can help elucidate characteristic patterns across these systems from water availability, transport, and use to energy generation, fuel supply, and customer demand, and in the interdependencies among these systems that can leave these systems vulnerable to cascading impacts from single disruptions. In this paper, we discuss ways in which data and machine learning can be applied to the challenges facing the energy-water nexus along with the potential issues associated with the machine learning techniques themselves. We then survey machine learning techniques that have found application to date in energy-water nexus problems. We conclude by outlining future research directions and opportunities for collaboration among the energy-water nexus and machine learning communities that can lead to mutual synergistic advantage.

scholarly journals Challenges and Opportunities in Applied Machine Learning

AI Magazine ◽  
2012 ◽  
Vol 33 (1) ◽  
pp. 11-24 ◽  
Author(s):  
Carla E. Brodley ◽  
Umaa Rebbapragada ◽  
Kevin Small ◽  
Byron Wallace
Keyword(s):  
Machine Learning ◽  
Academic Discipline ◽  
Learning Problems ◽  
Open Problems ◽  
Practical Applications ◽  
Applied Machine Learning ◽  
Fertile Ground ◽  
Challenges And Opportunities ◽  
Real World Problems

Machine learning research is often conducted in vitro, divorced from motivating practical applications. A researcher might develop a new method for the general task of classification, then assess its utility by comparing its performance (such as accuracy or AUC) to that of existing classification models on publicly available datasets. In terms of advancing machine learning as an academic discipline, this approach has thus far proven quite fruitful. However, it is our view that the most interesting open problems in machine learning are those that arise during its application to real-world problems. We illustrate this point by reviewing two of our interdisciplinary collaborations, both of which have posed unique machine learning problems, providing fertile ground for novel research.

Sign in / Sign up

Export Citation Format

Share Document