Correlation and clusterisation of traditional Malay musical instrument sound using the I-KAZTM statistical signal analysis

The best feature scheme is vital in musical instrument sound clustering and classification, as it is an input and feed towards the pattern recognition technique. This paper studies the relationship of every traditional Malay musical instrument acoustic sounds by implementing a correlation and cluste...

وصف كامل

التفاصيل البيبلوغرافية
الحاوية / القاعدة:Journal of Mechanical Engineering and Sciences
المؤلف الرئيسي: 2-s2.0-85032206518
التنسيق: مقال
اللغة:English
منشور في: Universiti Malaysia Pahang 2017
الوصول للمادة أونلاين:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032206518&doi=10.15282%2fjmes.11.1.2017.13.0234&partnerID=40&md5=e049e7ad86a27e284e3482f27f6a4a35
id Ahmad M.A.F.; Nuawi M.Z.; Bahari A.R.; Kechot A.S.; Saad S.M.
spelling Ahmad M.A.F.; Nuawi M.Z.; Bahari A.R.; Kechot A.S.; Saad S.M.
2-s2.0-85032206518
Correlation and clusterisation of traditional Malay musical instrument sound using the I-KAZTM statistical signal analysis
2017
Journal of Mechanical Engineering and Sciences
11
1
10.15282/jmes.11.1.2017.13.0234
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032206518&doi=10.15282%2fjmes.11.1.2017.13.0234&partnerID=40&md5=e049e7ad86a27e284e3482f27f6a4a35
The best feature scheme is vital in musical instrument sound clustering and classification, as it is an input and feed towards the pattern recognition technique. This paper studies the relationship of every traditional Malay musical instrument acoustic sounds by implementing a correlation and clustering method through the selected features. Two types of musical instruments are proposed, namely flutes involving key C and key G classes and caklempong consisting of gereteh and saua. Each of them is represented with a set of music notes. The acoustic music recording process is conducted using a developed design experiment that consists of a microphone, power module and data acquisition system. An alternative statistical analysis method, namely the Integrated Kurtosis-based Algorithm for Z-notch Filter (I-kazTM), denoted by the I-kaz coefficient, Z∞, has been applied and the standard deviation is calculated from the recorded music notes signal to investigate and extract the signal's features. Correlation and clustering is done by interpreting the data through Z∞ and the standard deviation in the regression analysis and data mining. The results revealed that a difference wave pattern is formed for a difference instrument on the time-frequency domain but remains unclear, thus correlation and clusterisation are needed to classify them. The correlation of determination, R2 ranging from 0.9291 to 0.9831, thus shows a high dependency and strong statistical relationship between them. The classification of flute and caklempong through mapping and clustering is successfully built with each of them separated with their own region area without overlapping, with statistical coefficients ranging from (2.79 x 10-10, 0.002932) to (1.64 x 10-8, 0.013957) for caklempong, while the flute measured from (2.45 x 10-9, 0.013143) to (1.92 x 10-6, 0.322713) in the x and y axis. © Universiti Malaysia Pahang, Malaysia.
Universiti Malaysia Pahang
22894659
English
Article
All Open Access; Gold Open Access; Green Open Access
author 2-s2.0-85032206518
spellingShingle 2-s2.0-85032206518
Correlation and clusterisation of traditional Malay musical instrument sound using the I-KAZTM statistical signal analysis
author_facet 2-s2.0-85032206518
author_sort 2-s2.0-85032206518
title Correlation and clusterisation of traditional Malay musical instrument sound using the I-KAZTM statistical signal analysis
title_short Correlation and clusterisation of traditional Malay musical instrument sound using the I-KAZTM statistical signal analysis
title_full Correlation and clusterisation of traditional Malay musical instrument sound using the I-KAZTM statistical signal analysis
title_fullStr Correlation and clusterisation of traditional Malay musical instrument sound using the I-KAZTM statistical signal analysis
title_full_unstemmed Correlation and clusterisation of traditional Malay musical instrument sound using the I-KAZTM statistical signal analysis
title_sort Correlation and clusterisation of traditional Malay musical instrument sound using the I-KAZTM statistical signal analysis
publishDate 2017
container_title Journal of Mechanical Engineering and Sciences
container_volume 11
container_issue 1
doi_str_mv 10.15282/jmes.11.1.2017.13.0234
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032206518&doi=10.15282%2fjmes.11.1.2017.13.0234&partnerID=40&md5=e049e7ad86a27e284e3482f27f6a4a35
description The best feature scheme is vital in musical instrument sound clustering and classification, as it is an input and feed towards the pattern recognition technique. This paper studies the relationship of every traditional Malay musical instrument acoustic sounds by implementing a correlation and clustering method through the selected features. Two types of musical instruments are proposed, namely flutes involving key C and key G classes and caklempong consisting of gereteh and saua. Each of them is represented with a set of music notes. The acoustic music recording process is conducted using a developed design experiment that consists of a microphone, power module and data acquisition system. An alternative statistical analysis method, namely the Integrated Kurtosis-based Algorithm for Z-notch Filter (I-kazTM), denoted by the I-kaz coefficient, Z∞, has been applied and the standard deviation is calculated from the recorded music notes signal to investigate and extract the signal's features. Correlation and clustering is done by interpreting the data through Z∞ and the standard deviation in the regression analysis and data mining. The results revealed that a difference wave pattern is formed for a difference instrument on the time-frequency domain but remains unclear, thus correlation and clusterisation are needed to classify them. The correlation of determination, R2 ranging from 0.9291 to 0.9831, thus shows a high dependency and strong statistical relationship between them. The classification of flute and caklempong through mapping and clustering is successfully built with each of them separated with their own region area without overlapping, with statistical coefficients ranging from (2.79 x 10-10, 0.002932) to (1.64 x 10-8, 0.013957) for caklempong, while the flute measured from (2.45 x 10-9, 0.013143) to (1.92 x 10-6, 0.322713) in the x and y axis. © Universiti Malaysia Pahang, Malaysia.
publisher Universiti Malaysia Pahang
issn 22894659
language English
format Article
accesstype All Open Access; Gold Open Access; Green Open Access
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