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.

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In ensemble music performance, such as a string quartet or duet, the musicians interact and influence each other’s performance via a multitude of parameters – including tempo, dynamics, articulation of musical phrases and, depending on the type of instrument, intonation. This paper presents our ongoing research on the effect of interaction between violinists, in terms of intonation. We base our analysis on a series of experiments with professional as well as amateur musicians playing in duet and solo experimental set-ups, and then apply a series of interdependence measures on each violinist’s pitch deviations from the score. Our results show that while it is possible to, solely based on intonation, distinguish between solo and duet performances for simple cases, there is a multitude of underlying factors that need to be analyzed before these techniques can be applied to more complex pieces and/or non-experimental situations.

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