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

M. A.F. Ahmad, Mohd. Zaki Nuawi, A. R. Bahari, Ab. Samad Kechot, Suziana Mat Saad

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)2552-2566
Number of pages15
JournalJournal of Mechanical Engineering and Sciences
Volume11
Issue number1
DOIs
Publication statusPublished - 1 Mar 2017

Fingerprint

Musical instruments
Signal analysis
Acoustic waves
Acoustics
Notch filters
Microphones
Regression analysis
Pattern recognition
Data mining
Data acquisition
Statistical methods
Experiments

Keywords

  • Classification
  • Clustering
  • Correlation
  • I-kaz statistical analysis
  • Malay musical instrument

ASJC Scopus subject areas

  • Computational Mechanics
  • Fuel Technology
  • Energy Engineering and Power Technology
  • Mechanics of Materials
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering

Cite this

@article{5256cd81e9504d82ab36caef19cee24c,
title = "Correlation and clusterisation of traditional Malay musical instrument sound using the I-KAZTM statistical signal analysis",
abstract = "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.",
keywords = "Classification, Clustering, Correlation, I-kaz statistical analysis, Malay musical instrument",
author = "Ahmad, {M. A.F.} and Nuawi, {Mohd. Zaki} and Bahari, {A. R.} and Kechot, {Ab. Samad} and {Mat Saad}, Suziana",
year = "2017",
month = "3",
day = "1",
doi = "10.15282/jmes.11.1.2017.13.0234",
language = "English",
volume = "11",
pages = "2552--2566",
journal = "Journal of Mechanical Engineering and Sciences",
issn = "2289-4659",
publisher = "Faculty of Mechanical Engineering, Universiti Malaysia Pahang",
number = "1",

}

TY - JOUR

T1 - Correlation and clusterisation of traditional Malay musical instrument sound using the I-KAZTM statistical signal analysis

AU - Ahmad, M. A.F.

AU - Nuawi, Mohd. Zaki

AU - Bahari, A. R.

AU - Kechot, Ab. Samad

AU - Mat Saad, Suziana

PY - 2017/3/1

Y1 - 2017/3/1

N2 - 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.

AB - 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.

KW - Classification

KW - Clustering

KW - Correlation

KW - I-kaz statistical analysis

KW - Malay musical instrument

UR - http://www.scopus.com/inward/record.url?scp=85032206518&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85032206518&partnerID=8YFLogxK

U2 - 10.15282/jmes.11.1.2017.13.0234

DO - 10.15282/jmes.11.1.2017.13.0234

M3 - Article

VL - 11

SP - 2552

EP - 2566

JO - Journal of Mechanical Engineering and Sciences

JF - Journal of Mechanical Engineering and Sciences

SN - 2289-4659

IS - 1

ER -