ABSTRACT
In a musical ensemble such as a string quartet, the musicians interact and influence each other's actions in several aspects of the performance simultaneously in order to achieve a common aesthetic goal. In this article, we present and evaluate a computational approach for measuring the degree to which these interactions exist in a given performance. We recorded a number of string quartet exercises under two experimental conditions (solo and ensemble), acquiring both audio and bowing motion data. Numerical features in the form of time series were extracted from the data as performance descriptors representative of four distinct dimensions of the performance: Intonation, Dynamics, Timbre, and Tempo. Four different interdependence estimation methods (two linear and two nonlinear) were applied to the extracted features in order to assess the overall level of interdependence between the four musicians. The obtained results suggest that it is possible to correctly discriminate between the two experimental conditions by quantifying interdependence between the musicians in each of the studied performance dimensions; the nonlinear methods appear to perform best for most of the numerical features tested. Moreover, by using the solo recordings as a reference to which the ensemble recordings are contrasted, it is feasible to compare the amount of interdependence that is established between the musicians in a given performance dimension across all exercises, and relate the results to the underlying goal of the exercise. We discuss our findings in the context of ensemble performance research, the current limitations of our approach, and the ways in which it can be expanded and consolidated.
ABSTRACT
This work presents a method for measuring and computing violin-body directional frequency responses, which are used for violin sound synthesis. The approach is based on a frame-weighted deconvolution of excitation and response signals. The excitation, consisting of bowed glissandi, is measured with piezoelectric transducers built into the bridge. Radiation responses are recorded in an anechoic chamber with multiple microphones placed at different angles around the violin. The proposed deconvolution algorithm computes impulse responses that, when convolved with any source signal (captured with the same transducer), produce a highly realistic violin sound very similar to that of a microphone recording. The use of motion sensors allows for tracking violin movements. Combining this information with the directional responses and using a dynamic convolution algorithm, helps to improve the listening experience by incorporating the violinist motion effect in stereo.
