Advancement in Graphical User Interface Tailored Quality Classification of Sape Soundboard

Document Type

Article

Publication Date

8-1-2024

Abstract

This research introduces an innovative methodology for evaluating and predicting soundboard quality in the intricate craftsmanship of sape instruments. Despite the sape's profound cultural significance, the process of selecting soundboard wood has been inadequately explored, resulting in uncertainties within the crafting community. Addressing this research gap, this study integrates advanced machine learning techniques and devises a specialized Graphical User Interface (GUI) tailored for sape makers. The methodology encompasses a thorough acoustic analysis of three distinct hardwoods-adau, merbau, and tapang-employing machine learning classification through Support Vector Machine with a Gaussian kernel. The study culminates in the development of a userfriendly GUI for soundboard quality assessment. Results underscore the model's proficiency for achieving an optimized accuracy of 87.8% in classifying sape audio samples. The MATLAB App Designer-based GUI streamlines the evaluation process, offering a practical and accessible tool for craftsmen. This integrated approach, harmonizing traditional craftsmanship with cutting-edge technology, holds the potential to revolutionize sape instrument manufacturing, ensuring the preservation and progressive evolution of this rich cultural heritage.

Keywords

Sape, Soundboard quality, Machine learning, Traditional musical instrument, Graphical user interface

Publication Title

BioResources

Divisions

mechanical

Funders

University of Technology Sarawak (4/2021/06) ; (2/2024/09)

Volume

19

Issue

3

Publisher

Department of Wood and Paper Science, North Carolina State University

Publisher Location

CAMPUS BOX 8005, RALEIGH, NC 27695-8005 USA

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