Proceedings of the International Conference for Phoenixes on Emerging Current Trends in Engineering and Management (PECTEAM 2018)

Classification Analysis for Musical Instrument Signal

Authors
A. Muthumari
Corresponding Author
A. Muthumari
Available Online February 2018.
DOI
https://doi.org/10.2991/pecteam-18.2018.2How to use a DOI?
Keywords
Enhanced Mel Frequency Cepstral Coefficient, Enhanced Power Normalized Cepstral Coefficients, Support Vector Machines, Linear Predictive Coefficients.
Abstract
The automatic musical instrument classification taking place in a recording of music has many applications, together with music search through classes, music recommender methods and transcribers. Automatic instrument classification and identification of musical streams has become a difficulty research area over the last few years. In this approach is to classify the audio data based on the instruments. The audio features such as Enhanced Mel Frequency Cepstral Coefficient (EMFCC) and Enhanced Power Normalized Cepstral Coefficients(EPNCC) are used to extract the features for classification of various instrument classes. The classification algorithms such as J48, BFTree, K Star, RandamForest and Bagging are used to classify musical instrument data into classes. Compare with various performance parameters like True Positive Rate, False Positive Rate etc., are used in various classification algorithms. The results shows that the best performance, almost 98% of accuracy, was attained by the classification system using the boosting technique with decision trees.
Open Access
This is an open access article distributed under the CC BY-NC license.

Download article (PDF)

Proceedings
International Conference for Phoenixes on Emerging Current Trends in Engineering and Management (PECTEAM 2018)
Part of series
Advances in Engineering Research
Publication Date
February 2018
ISBN
978-94-6252-492-7
ISSN
2352-5401
DOI
https://doi.org/10.2991/pecteam-18.2018.2How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - A. Muthumari
PY  - 2018/02
DA  - 2018/02
TI  - Classification Analysis for Musical Instrument Signal
BT  - International Conference for Phoenixes on Emerging Current Trends in Engineering and Management (PECTEAM 2018)
PB  - Atlantis Press
SN  - 2352-5401
UR  - https://doi.org/10.2991/pecteam-18.2018.2
DO  - https://doi.org/10.2991/pecteam-18.2018.2
ID  - Muthumari2018/02
ER  -