Score Informed Tonic Identification for Makam Music of Turkey

Tonic is a fundamental concept in many music traditions and its automatic identification should be relevant for establishing the reference pitch when we analyse the melodic content of the music. In this paper, we present two methodologies for the identification of the tonic in audio recordings of makam music of Turkey, both taking advantage of some score information. First, we compute a prominent pitch and a audio kernel-density pitch class distribution (KPCD) from the audio recording. The peaks in the KPCD are selected as tonic candidates. The first method computes a score KPCD from the monophonic melody extracted from the score. Then, the audio KPCD is circularshifted with respect to each tonic candidate and compared with the score KPCD. The best matching shift indicates the estimated tonic. The second method extracts the monophonic melody of the most repetitive section of the score. Normalising the audio prominent pitch with respect to each tonic candidate, the method attempts to link the repetitive structural element given in the score with the respective time-intervals in the audio recording. The result producing the most confident links marks the estimated tonic. We have tested the methods on a dataset of makam music of Turkey, achieving a very high accuracy (94.9%) with the first method, and almost perfect identification (99.6%) with the second method. We conclude that score informed tonic identification can be a useful first step in the computational analysis (e.g. expressive analysis, intonation analysis, audio-score alignment) of music collections involving melody-dominant content.