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| Content Provider | ACM Digital Library |
|---|---|
| Author | Zaccagnino, Gianluca Zaccagnino, Rocco Malandrino, Delfina De Prisco, Roberto |
| Abstract | Today's technology is redefining the way individuals can work, communicate, share experiences, constructively debate, and actively participate to any aspect of the daily life, ranging from business to education, from political and intellectual to social, and so on. Enabling access to technology by any individual, reducing obstacles, avoiding discrimination, and making the overall experience easier and enjoyable is an important objective of both research and industry. Exploiting natural user interfaces, initially conceived for the game market, it is possible to enhance the traditional modalities of interaction when accessing to technology, build new forms of interactions by transporting users in a virtual dimension, but that fully reflects the reality, and finally, improve the overall perceived experience. The increasing popularity of these innovative interfaces involved their adoption in other fields, including Computer Music. This paper presents MarcoSmiles, a system designed to allow individuals to perform music in a easy, innovative, and personalized way. The idea is to design new interaction modalities during music performances by using hands without the support of a real musical instrument. We exploited Artificial Neural Networks to customize the virtual musical instrument, to provide the information for the mapping of the hands configurations into musical notes and, finally, to train and test these configurations. We studied the behavior of the system and its efficacy in terms of learning capabilities. We also report results about a preliminary evaluation study aimed at analyze general users' perceptions about the system and their overall satisfaction. |
| Starting Page | 204 |
| Ending Page | 211 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781450341318 |
| DOI | 10.1145/2909132.2909249 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2016-06-07 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Evaluation Neural networks Natural user interfaces Music performance |
| Content Type | Text |
| Resource Type | Article |
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