A Hidden Markov Model of Melody in Greek Church Chant
نویسنده
چکیده
We present a probabilistic model of melodic process in modern Greek church chant. This largely oral tradition often relies on memorization and improvisation skills that are passed on from teacher to student by example, without explicit appeal to rules. The researcher is thus faced with the challenge of inferring the rules of the idiom from a sample corpus of chants. The structure of the rules will point to the mental representation of melody that underlies learning, recall, and improvisation. Our analysis is performed in two stages. In the first stage, a Hidden Markov Model (HMM) is trained on the corpus of chants, using a variant of the algorithm developed by Stolcke and Omohundro. As a termination criterion for this training stage, we use Rissanen’s Minimum Description Length principle. In the second stage, the optimal HMM is analyzed; its states can be interpreted as probabilistic rules that determine the course of melody, given its preceding melodic and textual context. Our findings show that the melody of Greek chant is shaped by textual word stress on a small scale, and by textual syntactic boundaries on a large scale. Moreover, given the pattern of textual word stress and syntactic grouping, the shaping of the melody within a given mode is completely determined by a small number of phrase parameters, reflecting melodic choices at key decision points. We discuss the relation of our model to earlier cognitive models of melody, especially that of Deutsch and Feroe.
منابع مشابه
Introducing Busy Customer Portfolio Using Hidden Markov Model
Due to the effective role of Markov models in customer relationship management (CRM), there is a lack of comprehensive literature review which contains all related literatures. In this paper the focus is on academic databases to find all the articles that had been published in 2011 and earlier. One hundred articles were identified and reviewed to find direct relevance for applying Markov models...
متن کاملIntrusion Detection Using Evolutionary Hidden Markov Model
Intrusion detection systems are responsible for diagnosing and detecting any unauthorized use of the system, exploitation or destruction, which is able to prevent cyber-attacks using the network package analysis. one of the major challenges in the use of these tools is lack of educational patterns of attacks on the part of the engine analysis; engine failure that caused the complete training, ...
متن کاملA generalization of Profile Hidden Markov Model (PHMM) using one-by-one dependency between sequences
The Profile Hidden Markov Model (PHMM) can be poor at capturing dependency between observations because of the statistical assumptions it makes. To overcome this limitation, the dependency between residues in a multiple sequence alignment (MSA) which is the representative of a PHMM can be combined with the PHMM. Based on the fact that sequences appearing in the final MSA are written based on th...
متن کاملA Novel HMM Approach to Melody Spotting in Raw Audio Recordings
This paper presents a melody spotting system based on Variable Duration Hidden Markov Models (VDHMM’s), capable of locating monophonic melodies in a database of raw audio recordings. The audio recordings may either contain a single instrument performing in solo mode, or an ensemble of instruments where one of the instruments has a leading role. The melody to be spotted is presented to the syste...
متن کامل