A Hierarchical Approach to Makam Classification of Turkish Makam Music, Using Symbolic Data

Abstract

A method for hierarchical classification of makams from symbolic data is presented. A makam generally implies a miscellany of rules for melodic composition using a given scale. Therefore, makam detection is to some level similar to the key detection problem. The proposed algorithm classifies makams by applying music theoretical knowledge and statistical evidence in a hierarchical manner. The makams using similar scales are first grouped together, and then identified in detail later. The first level of the hierarchical decision is based on statistical information provided by the n-gram likelihood of the symbolic sequences. A cross-entropy based metric, perplexity, is used to calculate similarity between makam models and the input music piece. Later, using statistical features related to the content of the piece, such as the tonic note, the average pitch level for local excerpts and the overall pitch progression, a more detailed identification of the makam is achieved. Different length n-grams and representation paradigms are used, including the Arel theory, the 12 tone equal tempered representation, and interval contour. Results show that the hierarchical approach is better, compared to a straightforward n-gram classification, for the makams which have similar pitch space, such as Huseyni-Muhayyerand Rast-Mahur. Using the proposed methodology, the system's recall rate increases from 88.7% to 90.9% where there exists still some confusion between the makams Uak and Beyati.