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Authors:
Vanessa Yaremchuk, Carolina B. Medeiros, Marcelo M. WanderleyPublication or Conference Title:
Proceedings of the 2019 International Conference on New Interfaces for Musical Expression (NIME 2019)Abstract:
The Rulers is a Digital Musical Instrument with 7 metal beams, each of which is fixed at one end. It uses infrared sensors, Hall sensors, and strain gauges to estimate deflection. These sensors each perform better or worse depend- ing on the class of gesture the user is making, motivating sensor fusion practices. Residuals between Kalman filters and sensor output are calculated and used as input to a re- current neural network which outputs a classification that determines which processing parameters and sensor measurements are employed. Multiple instances (30) of layer recurrent neural networks with a single hidden layer vary- ing in size from 1 to 10 processing units were trained, and tested on previously unseen data. The best performing neu- ral network has only 3 hidden units and has a sufficiently low error rate to be good candidate for gesture classification.
This paper demonstrates that: dynamic networks out- perform feedforward networks for this type of gesture classification, a small network can handle a problem of this level of complexity, recurrent networks of this size are fast enough for real-time applications of this type, and the importance of training multiple instances of each network architecture and selecting the best performing one from within that set.
Publication Details:
Type: |
Conference Paper |
Date: |
06/01/2019 |
Pages: |
150-155 |
Location: |
Porto Alegre, Brazil |
DOI: |
10.5281/zenodo.3672904 |
