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Acoustics, Volume 5, Issue 3
September 2023 - 14 articles
Cover Story: Percussionists strive to achieve their desired sound by mounting various products on the surface of their drums, commonly through laborious trial-and-error methods. In an effort to establish a benchmark for addressing this challenge, a dataset containing numerous sounds was generated by Finite-Difference Time-Domain (FDTD) models. These sounds correspond to different patterns of mass increase on the drum surface, and are investigated to reveal correlations between sound spectra and distribution patterns using dimensionality reduction techniques. Ultimately, a Convolutional Neural Network (CNN) is employed to infer the damping and tuning strategy corresponding to an input sound, thus demonstrating the effectiveness of data-driven approaches in tackling problems related to inverse acoustics. View this paper
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Acoustics - ISSN 2624-599X