Computational Approaches for Melodic Description in Indian Art Music Corpora
|Title||Computational Approaches for Melodic Description in Indian Art Music Corpora|
|Publication Type||PhD Thesis|
|Year of Publication||2016|
|University||Universitat Pompeu Fabra|
|Academic Department||Department of Information and Communication Technologies|
|Number of Pages||305|
|Date Published||Under review|
|Keywords||audio analysis, carnatic, chalan, CompMusic, computational analysis, dtw, dynamic time warping, hindustani, Indian art music, melodic description, melodic patterns, Melody, motifs, music information retrieval, Nyas, pakad, pattern network, patterns, phrases, raaga, raga, raga recognition, svara, time delayed melodic surface, Time series, Tonic identification, vector space modeling|
Automatically describing contents of recorded music is crucial for interacting with large volumes of audio recordings, and for developing novel tools to facilitate music pedagogy. Melody is a fundamental facet in most music traditions and, therefore, is an indispensable component in such description. In this thesis, we develop computational approaches for analyzing high-level melodic aspects of music performances in Indian art music (IAM), with which we can describe and interlink large amounts of audio recordings. With its complex melodic framework and well-grounded theory, the description of IAM melody beyond pitch contours offers a very interesting and challenging research topic. We analyze melodies within their tonal context, identify
The thesis starts by compiling and structuring largest to date music corpora of the two IAM traditions, Hindustani and Carnatic music, comprising quality audio recordings and the associated metadata. From them we extract the predominant pitch and normalize by the tonic context. An important element to describe melodies is the identification of the meaningful temporal units, for which we propose to detect occurrences of nyās svaras in Hindustani music, a landmark that demarcates musically salient melodic patterns.
Utilizing these melodic features, we extract musically relevant recurring melodic patterns. These patterns are the building blocks of melodic structures in both improvisation and composition. Thus, they are fundamental to the description of audio collections in IAM. We propose an unsupervised approach that employs time-series analysis tools to discover melodic patterns in sizable music collections. We first carry out an in-depth supervised analysis of melodic similarity, which is a critical component in pattern discovery. We then improve upon the best possible competing approach by
Finally, we utilize our results for recognizing rāgas in recorded performances of IAM. We propose two novel approaches that jointly capture the tonal and the temporal aspects of melody. Our first approach uses melodic patterns, the most prominent cues for rāga identification by humans. We utilize the discovered melodic patterns and employ topic modeling techniques, wherein we regard a rāga rendition similar to a textual description of a topic. In our second approach, we propose the time delayed melodic surface, a novel feature based on delay coordinates that captures the melodic outline of a rāga. With these approaches we demonstrate unprecedented accuracies in rāga recognition on the largest datasets ever used for this task. Although our approach is guided by the characteristics of melodies in IAM and the task at hand, we believe our methodology can be easily extended to other melody dominant music traditions.
Overall, we have built novel computational methods for analyzing several melodic aspects of recorded performances in IAM, with which we describe and interlink large amounts of music recordings. In this process we have developed several tools and compiled data that can be used for a number of computational studies in IAM, specifically in characterization of rāgas, compositions and artists. The technologies resulted from this research work are a part of several applications developed within the CompMusic project for a better description, enhanced listening experience, and pedagogy in IAM.