Learning Indirect Acquisition of Instrumental Gestures using Direct Sensors

Sensing instrumental gestures is a common task in interactive electroacoustic music performances. The sensed gestures can then be mapped to sounds, synthesis algorithms, visuals etc. Two of the most common approaches for acquiring these gestures are: 1) Hybrid instruments which are "traditional" musical instruments enhanced with sensors that directly detect gestures 2) Indirect acquisition in which the only measurement is the acoustic signal and signal processing techniques are used to acquire the gestures. Hybrid instruments require modification of existing instruments which is frequently undesirable. However they provide relatively straightforward and reliable measuring capability. On the other hand, indirect acquisition approaches typically require sophisticated signal processing and possibly machine learning algorithms in order to extract the relevant information from the audio signals. In this paper the idea of using direct sensors to train a machine learning model for indirect acquisition is explored. This approach has some nice advantages, mainly: 1) large amounts of training data can be collected with minimum effort 2) once the indirect acquisition system is trained no sensors or modifications to the playing instrument are required. Case studies described in paper include 1) strike position on a snare drum 2) strum direction on a sitar