This research aims to provide a systematic approach for the analysis of geometrical and material characteristics of traditional frame drums using deep learning. A data-driven approach is used, integrating supervised and unsupervised feature extraction techniques to associate measurable audio features with perceptual attributes. The methodology involves the training of convolutional neural networks on Mel-Scale spectrograms to estimate wood type (classification), diameter (regression), and depth (regression). A multi-labeled dataset containing recorded samples of frame drums of different specifications is used for model training and evaluation. Hierarchical classification is explored, incorporating playing techniques and environmental factors. Handcrafted features enhance interpretability, helping determine the impact of construction attributes on sound perception, ultimately aiding instrument design. Data augmentation techniques, including pitch alterations, additive noise, etc. are introduced to expand the generalization of the approach and dataset expansion.
(See document for exact affiliation information.)
Click to purchase paper as a non-member or login as an AES member. If your company or school subscribes to the E-Library then switch to the institutional version. If you are not an AES member Join the AES. If you need to check your member status, login to the Member Portal.
