Feature Extraction for Traditional Malay Musical Instruments Classification System

Automatic musical instrument classification system deals with a large number of sound database and various types of features schemes. With the lack of data pre-processing, it might become invaluable asset that can impact the whole classification tasks. In handling an effective classification system, finding the best data sets with the best features schemes often a vital step in the data representation and feature extraction process. Thus, this study is conducted in order to investigate the impact of several factors that might affecting the classification accuracy such as audio length, segmented frame size and data sets size (for training and testing) towards traditional Malay musical instruments sounds classification system. The perception-based and MFCC features schemes with total of 37 features was utilized in this study. Meanwhile, multi-layered perceptrons classifier is employed to evaluate the modified data sets and extracted features schemes in terms of their classification performance. Results show that the highest accuracy of 99.57% was obtained from the best data sets with the combination of full features. It is also revealed that the identified factors had a significant role to the performance of classification accuracy. Hence, this study suggest that further feature analysis research is necessary for better solution in traditional Malay musical instruments sounds classification system problem.