Stage M2 – Improving trumpet sound simulations by data-driven modelling techniques

Sound simulation by physical modelling relies on the representation of the physics underlying the functioning of musical instrument. It constitutes an interesting tool to simulate the variety of behaviors of the instrument, and also a very useful tool for musical instrument makers in order to better understand the influence of the different design parameters on the response of the system (sound, blowing pressure, etc.). Indeed, the instrument designer can investigate numerically the consequences of changes in the conception on the response of the instrument, without having to build a physical prototype. This numerical aid is therefore very interesting to investigate various design choices.

Furthermore, one challenge in developing such physical models relies on the modelling of the players, that is to say of the lips for brass instruments. Indeed, the lips constitute a sophisticated biomechanical system that are currently modelled using simple linear or nonlinear mechanical oscillators.

Although these models provide relatively satisfactory performances, the parameters of these models (resonance frequencies, damping, mass) remain difficult to estimate precisely. Besides, measurements to identify these parameters are very difficult (or even impossible) to conduct without perturbating the player.

To try to overcome this issue, experimental systems such as artificial players have been developed at LMA and in Yamaha laboratories in order to play instruments in controlled conditions, and to monitor a number of variables (pressure, lip displacement, etc.) during the performance.

The objective of this project is to study the performance of a data-driven system identification technique to identify lip models and their parameters using experimental data collected on an artificial trumpet player developed at LMA. This approach relies on combining sparsity-promoting techniques and machine learning with nonlinear dynamical systems, to discover the governing physical equations from measurement data.

More informations on the link below.