| Abstract |
The field of computational ethnomusicology has drawn growing attention by researchers in the music information retrieval community. In general, subjects are considered that are related to the processing of traditional forms of music, often with the goal to support studies in the field of musicology with computational means.
Tools have been proposed that make access to large digital collections of traditional music easier, for example by automatically detecting a specific kind of similarity between pieces or by automatically segmenting data into partitions that are either relevant or irrelevant for further investigation.
In this thesis, the focus lies on music of the Eastern Mediterranean, and specifically on traditional music of Greece and Turkey. At the beginning of the thesis related work, the task was defined which directed the aspects of the necessary research activities.
The task was motivated by the geographical location of the author, the island of Crete in Greece, but in the course of the thesis this task proved to have strong relevance for a much wider musical context: Given a polyphonic recording of a piece of Cretan traditional dance music, find a recording that is similar to it. Theory of musicology provided us with the way to approach this task.
The traditional music encountered in Greece and in wide parts of the Balkan states and Turkey as well, follows the logic of parataxis, which means that pieces are constructed by temporally aligning short musical phrases, without the existence of structures present in classical music or popular music. Thus, a system that is designed to cope with the above mentioned task has to be able to estimate the similarity of such phrases. As we deal with polyphonic audio signals of music that has not been written to a score, at least not before the performance, we need to do some simplification.
This is because the exact transcription of the main melody from a polyphonic mixture into a score is still an unsolved problem. And on the other side, the transcription of traditional music even by human experts is an extremely complex and difficult process. For that reason, a system has been designed that considers aspects of rhythm, timbre and melody for approaching the task.
The central aspect that has been considered in this thesis is rhythm. For this, a point of major interest is the estimation at which time instances within an audio signal a musical instrument starts playing a note. This estimation is referred to as onset detection, and has been approached in this thesis using novel group delay and fundamental frequency based approaches, and with a fusion of these characteristics with an spectral amplitude criterion. With these findings in the field of onset detection, improved beat trackers and rhythmic similarity estimation techniques are developed. The proposed beat tracker applies the group delay based onset detection method in the context of a state-of-the-art approach for beat tracking. Results show clear improvements when applying this method for beat tracking on a dataset of traditional music.
The rhythmic similarity estimation is based on scale transformation, which avoids the influence of tempo differences between pieces of music that are to be compared. On datasets containing Greek and Turkish traditional music high accuracies in a classification task are achieved, and the validity of the proposed measure as a similarity measure is supported by the results of listening tests.
Apart from rhythm, also the aspect of instrumental timbre has been addressed. A novel feature set based on Non-negative Matrix Factorization (NMF) is proposed to describe the characteristic spectral bases of a piece of music. These bases are modeled using statistical methods, and it is shown that these models describe the spectral space of musical genres and instrumental classes in a compact and discriminative way.
Finally, melodic aspects have been considered as well by combining state-of-the-art approaches for cover song detection in popular music and fundamental frequency detection from polyphonic signals. This combination is shown to tackle the central task of the thesis work in a satisfying way on a small exemplary dataset. A morphological analysis framework that combines the aspects of rhythm, timbre and melody is proposed, which can be used to detect similarities in traditional music.
For the development of the algorithms presented in this thesis, evaluation data had to be collected. This was a task of major difficulty and much effort has been made by the author to understand well the musical context that is investigated in this thesis. For many datasets, the ground truth was achieved in cooperation with local musicians in time-consuming but very informative interviews. The knowldedge obtained in these interviews and the resulting datasets are another important contribution of this thesis.
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