R aga forms the melodic framework for most of the music of the Indian subcontinent. Thus automatic r aga recognition is a fundamental step in the computational modelling of the Indian art-music traditions. In this work, we investigate the properties of r aga and the natural processes by which people identify it. We bring together and discuss the previous computational approaches to r aga recognition correlating them with human techniques, in both Karn _ at _ aka (south Indian) and Hindust an ı (north Indian) music traditions. The approaches which are based on first-order pitch distributions are further evaluated on a large comprehensive dataset to understand their merits and limitations. We outline the possible short and mid-term future directions in this line of work.

Intonation is a fundamental music concept that has a special relevance in Indian art music. It is characteristic of a rāga and key to the musical expression of the artist. Describing intonation is of importance to several music information retrieval tasks such as developing similarity measures based on rāgas and artists. In this paper, we first assess rāga intonation qualitatively by analysing varn . aṁs, a particular form of Carnatic music compositions. We then approach the task of automatically obtaining a compact representation of the intonation of a recording from its pitch track. We propose two approaches based on the parametrization of pitch-value distributions: performance pitch histograms, and context-based svara distributions obtained by categorizing pitch contours based on the melodic context. We evaluate both approaches on a large Carnatic music collection and discuss their merits and limitations. We finally go through different kinds of contextual information that can be obtained to further improve the two approaches.

Abstract. Computational approaches that conform to the cultural context are of paramount importance in music information research. The current state-of-theart has a limited view of such context, which manifests in our ontologies, data-, cognition-and interaction-models that are biased to the market-driven popular music. In a step towards addressing this, the thesis draws upon multimodal data sources concerning art music traditions, extracting culturally relevant and musically meaningful information about melodic intervals from each of them and structuring it with formal knowledge representations. As part of this, we propose novel approaches to describe intonation in audio music recordings and to use and adapt the semantic web infrastructure to complement this with the knowledge extracted from text data. Due to the complementary nature of the data sources, structuring and linking the extracted information results in a symbiosis mutually enriching their information. Over this multimodal knowledge base, we propose similarity measures for the discovery of musical entities, yielding a culturallysound navigation space.

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