Classification of Iranian traditional musical modes (DASTGÄH) with artificial neural network

Abstract

The concept of Iranian traditional musical modes, namely DASTGÄH, is the basis for the traditional music system. The concept introduces seven DASTGÄHs. It is not an easy process to distinguish these modes and such practice is commonly performed by an experienced person in this field. Apparently, applying artificial intelligence to do such classification requires a combination of the basic information in the field of traditional music with mathematical concepts and knowledge. In this paper, it has been shown that it is possible to classify the Iranian traditional musical modes (DASTGÄH) with acceptable errors. The seven Iranian musical modes including SHÖR, HOMÄYÖN, SEGÄH, CHEHÄRGÄH, MÄHÖR, NAVÄ and RÄST-PANJGÄH are studied for the two musical instruments NEY and Violin as well as for a vocal song. For the purpose of classification, a multilayer perceptron neural network with supervised learning method is used. Inputs to the neural network include the top twenty peaks from the frequency spectrum of each musical piece belonging to the three aforementioned categories. The results indicate that the trained neural networks could distinguish the DASTGÄH of test tracks with accuracy around 65% for NEY, 72% for violin and 56% for vocal song.

Keywords

• Iranian traditional musical modes (DASTGÄH)
• Classification
• Artificial neural network
• Feature Extraction