Physics of musical drum head damping using externally applied products

Worland, Randy; Miyahira, William

doi:10.1121/2.0001011

Cited by 2 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are no standard instructions on how drums are damped, so the choice of which dampers to use and how to position them on the instrument is entirely up to the performer. The general idea is that a dampener will reduce the energy and duration of the modes that have antinodes at the membrane location of the dampener [2]. So circular dampeners aim at damping angular modes, small rectangular pads aim at damping diametric modes, while damping pads covering the entire membrane area provide volume reduction, without considerable changes of the sound texture.…”

Section: Introductionmentioning

confidence: 99%

Machine learning of Finite-Difference Time Domain (FDTD) physical modelling sound simulations of drumhead paste pattern distributions

Alexandraki,

Starakis,

Bader

et al. 2022

Proceedings of the 10th Convention of the European Acoustics Association Forum Acusticum 2023

View full text Add to dashboard Cite

Drummers attach different kinds of material on their drumheads to either increase damping or to tune them and adjust the relationships of sound partials. The former is a common practice for drummers, while the latter may be found in percussion instruments of various ethnic traditions, such as the Myanmar pat wain drum circle or the Indian tabla. A Finite-Difference Time Domain (FDTD) physical model of a drumhead was used to compute more than 2000 sounds simulating membrane vibration, which was adjusted by adding varied amounts of paste, distributed in different surface patterns. These sounds were analysed using Self-Organizing Maps (SOMs) as well as a Convolutional Neural Network (CNN). The SOMs were used to cluster the partial relationships of the generated sounds. It is demonstrated that different paste patterns correspond to different clusters. Furthermore, the CNN was trained to identify the damping approach, yielding an accuracy of 94% for paste pattern classification and a mean error of +/-11% for the estimation of membrane mass increase. These tools can be used to identify damping patterns used in historical drum recordings or as suggestions to percussionists for deriving a desired sound texture.

show abstract

Section: Introductionmentioning

confidence: 99%

Machine learning of Finite-Difference Time Domain (FDTD) physical modelling sound simulations of drumhead paste pattern distributions

Alexandraki,

Starakis,

Bader

et al. 2022

Proceedings of the 10th Convention of the European Acoustics Association Forum Acusticum 2023

View full text Add to dashboard Cite

show abstract

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

Alexandraki,

Starakis,

Zervas

et al. 2023

Acoustics

View full text Add to dashboard Cite

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.

show abstract

Physics of musical drum head damping using externally applied products

Cited by 2 publications

References 0 publications

Machine learning of Finite-Difference Time Domain (FDTD) physical modelling sound simulations of drumhead paste pattern distributions

Machine learning of Finite-Difference Time Domain (FDTD) physical modelling sound simulations of drumhead paste pattern distributions

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

Contact Info

Product

Resources

About