MELODY TRAINING WITH SEGMENT-BASED TILT CONTOUR FOR QURANIC TARANNUM

Abstract

Tarannum, or melodic recitation of Quranic verses, employs the softness of the voice in reading the holy verses of the Quran. Melody training technology allows users to practise repetitively while also providing feedback on their performance. This paper describes an application that captures the pattern of tarannum melodies (from Quranic recitations) and provides feedback to the user. Recordings of Quranic verses are collected from an expert reciting Bayati tarannum. The samples are pre-processed into segmented tarannum verse-contours using pitch sequences. Using the k-Nearest Neighbor (kNN) classifier, the melody patterns are trained on 20 samples. Input vectors are formed by computing the melody verse-contour representation using mean, standard deviation, and slope values and combining them with an identified Tilt-based contour label. A tarannum training prototype is built to test similarity between a user’s recitation and the trained patterns. To identify similarity between a pair of verse-contours, the application employs a shape-based contour similarity algorithm. The proposed application also provides feedback in the form of a grade and a percentage of accuracy, as determined by a melody curve similarity algorithm. As results, the current samples have an overall shape-based weighted score of 66%. Some samples are successfully classified with a similarity score as high as 80% individually. The study provides an alternative interactive session for people who want to learn Tarannum, as well as a preliminary step toward understanding the melodic patterns for tarannum. The application provides a repetitive training experience and encourages users to improve their recitations in order to achieve the highest possible score.