Augusto Armondes

GuiaRT is an interactive musical setup based on a nylon-string guitar equipped with hexaphonic piezoelectric pickups. It consists of a modular set of real-time tools for the symbolic transcription, variation, and triggering of selected segments during a performance, as well as some audio processing capabilities. Its development relied on an iterative approach, with distinct phases dedicated to feature extraction, transcriptions, and creative use. This article covers the motivations for this augmented instrument and several details of its implementation, including the hardware and strategies for identifying the most typical types of sound produced on a nylon-string guitar, as well as tools for symbolic musical transformations. This acoustic–digital interface was primarily designed for interactive exploration, and it has also been effectively used in performance analyses and as a pedagogical tool.

Computing technologies have opened up a myriad of possibilities for expanding the sonic capabilities of acoustic musical instruments. Musicians nowadays employ a variety of rather inexpensive, wireless sensor-based systems to obtain refined control of interactive musical performances in actual musical situations like live music concerts. It is essential though to clearly understand the capabilities and limitations of such acquisition systems and their potential influence on high-level control of musical processes. In this study, we evaluate one such system composed of an inertial sensor (MetaMotionR) and a hexaphonic nylon guitar for capturing strumming gestures. To characterize this system, we compared it with a high-end commercial motion capture system (Qualisys) typically used in the controlled environments of research laboratories, in two complementary tasks: comparisons of rotational and translational data. For the rotations, we were able to compare our results with those that are found in the literature, obtaining RMSE below 10° for 88% of the curves. The translations were compared in two ways: by double derivation of positional data from the mocap and by double integration of IMU acceleration data. For the task of estimating displacements from acceleration data, we developed a compensative-integration method to deal with the oscillatory character of the strumming, whose approximative results are very dependent on the type of gestures and segmentation; a value of 0.77 was obtained for the average of the normalized covariance coefficients of the displacement magnitudes. Although not in the ideal range, these results point to a clearly acceptable trade-off between the flexibility, portability and low cost of the proposed system when compared to the limited use and cost of the high-end motion capture standard in interactive music setups.