Mapping Blowing Pressure and Sound Features in Recorder Playing

This paper presents a data-driven approach to the construction of mapping models relating sound features and blowing pressure in recorder playing. Blowing pressure and sound feature data are synchronously obtained from real performance: blowing pressure is measured by means of a piezoelectric transducer inserted into the mouth piece of a modified recorder, while produced sound is acquired using a close-field microphone. Acquired sound is analyzed frame-by-frame, and features are extracted so that original sound can be reconstructed with enough fidelity. A multi-modal database of aligned blowing pressure and sound feature signals is constructed from real performance recordings designed to cover basic performance contexts. Out of the gathered data, two types of mapping models are constructed using artificial neural networks: (i) a model able to generate sound feature signals from blowing pressure signals, and therefore used to produce synthetic sound from recorded blowing pressure profiles via additive synthesis; and (ii) a model able to estimate the blowing pressure from extracted sound features.