Inferring Drumhead Damping and Tuning from Sound Using Finite Difference Time Domain (FDTD) Models

Abstract

Percussionists use a multitude of objects and materials, mounted on their instruments, to achieve a satisfying sound texture. This is a tedious process as there are no guidelines suggesting how to manipulate a percussion instrument to adjust its perceptual characteristics in the desired direction. To this end, the article presents a methodology for computationally identifying how to damp and tune a drumhead by adjusting its mass distribution, e.g., by applying malleable paste on its surface. A dataset of 11,114 sounds has been synthesized using a FDTD solution of the wave equation representing the vibration of a membrane, which is being transmuted through the application of paste. These sounds are investigated to derive conclusions concerning their spectral characteristics and data reduction techniques are used to investigate the feasibility of computationally inferring damping parameters for a given sound. Furthermore, these sounds are used to train a Convolutional Neural Network to infer mass distribution from sound. Results show that computational approaches can provide valuable information to percussionists striving to adjust their personal sound. Although this study has been performed with synthesized sounds, the research methodology presents some inspiring ideas for future investigations with prerecorded sounds.