A fuzzy hierarchy-based pattern matching technique for melody classification

Abstract

Classification of classical melodic structures by style, composer, genre, period, etc., is a rather complex task. The level of difficulty varies across melodic frameworks. It would be interesting to see how we can impart this ability to a machine. This paper reports an improved pattern matching technique for composer and raga classification using a fuzzy analytical hierarchy process-based approach. The technique makes use of class-specific patterns extracted from a pattern discovery technique known as Structure Induction Algorithm for r superdiagonals and compactness trawler. Further, to represent inexact matches a modified matching technique is proposed to assign weights to the exact matching scores in a probabilistic manner. Subsequently, the weighted scores are fuzzified to quantify the extent of match. Finally, the fuzzy scores are aggregated and classified on the basis of minimum Euclidean distance from an ideal solution in the pattern space. Experiments conducted on datasets containing a wide range of melodies from classical western and classical Indian background show that the proposed technique exhibits consistently better classification success rate compared to the exact n-Gram-based approach and a widely used matching algorithm based on Levenshtein distance.