Advanced instrumentation and sensor fusion methods in input devices for musical expression

Advanced electronic instrumentation and sensor signal processing in Input Devices for Musical Expression must meet several requirements: they should be accurate, reproducible, monotonic, and robust.Despite these demanding design requirements, a large number of input devices are currently being developed in a Do-It-Yourself manner using simple sensing techniques.The aim of this thesis is to raise awareness of the limitations of this approach.As a solution, we propose state-of-the-art engineering tools to improve the sensing design: use of specialized sensor technologies, better electronic instrumentation, coherent calibration and data regression methods, and advanced signal processing through the sensor fusion filters.We have reviewed the Proceedings of the International Conference on New Musical Interfaces for Musical Expression, the major academic event in this field, from 2009 to 2013.Based on this review, we identify the generalized use of unsophisticated engineering solutions and easily available sensors, which are simple to assemble and require uncomplicated signal conditioning circuits.We then propose several methods of instrumentation and sensor signal processing that can deal with the above issues.Using these solutions, we evaluate the sensing design of one Digital Musical Instrument -The Rulers, an instrument containing several beams that can be bent or plucked, where beam motion can be assessed by either infrared, Hall effect, or strain gage sensors.We show that none of them are an optimal measurement solution.We then take advantage of the best features of each sensor technology, by applying sensor fusion techniques, optimally achieved by a linear Kalman filter.However, the Kalman filter implementation on human input signals is not obvious because several parameters of the system and its operation modes cannot be predefined, given the unpredictable nature of these signals.We therefore propose a framework for Kalman filter application based on gesture segmentation and classification, multiple sensors, multiple-model system and measurements, several candidate process models, filter evaluation, and Monte Carlo optimization.We confirm the validity of this framework by showing that it reduces the error covariance of the estimate.These results will hopefully lead to robust, reproducible, and responsive input devices more likely to provide skilled performers with instruments that could rival acoustic musical instruments in terms of expressive potential.First, I would like to have 1/0 space here to thank not only those that directly contributed for this work, but also to those that help me on the adventure of learning, i.e. living.This thesis counts with many contributions.The main one is the wise thoughts of my supervisor, Marcelo M. Wanderley, which are inspiring lessons for life.I am also indebted to Darryl Cameron, Yves Methot, Jullien Boissinot and Harold Kilianski, for their friendly help.I wish to thank the engineers that helped on assembling mechanical

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