Gopala Krishna Koduri

Rāga Recognition based on Pitch Distribution Methods

Abstract Rāga forms the melodic framework for most of the music of the Indian subcontinent. Thus automatic rāga recognition is a fundamental step in the computational modelling of the Indian art-music traditions. In this work, we investigate the properties of rāga and the natural processes by which people identify it. We bring together and discuss the previous computational approaches to rāga recognition correlating them with human techniques, in both Karṇāṭaka (south Indian) and Hindustānī (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 Analysis of Rāgas in Carnatic Music

Abstract Intonation is a fundamental music concept that has a special relevance in Indian art music. It is characteristic of a rga 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 rgas and artists. In this paper, we first assess rga intonation qualitatively by analysing varṇaṁs, 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.

A behavioral study of emotions in south indian classical music andits implications in music recommendation systems

In order to model a culture-specific content-based music recommendatio/n system, a total of 750 subjective emotional responses to tunes composed in popular raagas of South Indian classical (Carnatic) music are empirically investigated to find out the long speculated relation between raagas (indian music scales) and rasas (emotion clusters). We discuss the results from analysis of this survey, which show that raagas are quite useful as a first step in a different direction towards content-based music recommendation. Along the way, we discriminate Carnatic and North-Indian classical (Hindustani) music traditions. We also convey the definition of rasa, which is different from being a single emotional state. We use a classification based on a novel approach in conceptualization of emotions based on navarasa, which is a emotion classification given by Bharata, that suits behavioral studies with Indian arts. Pitch-class profiles which were previously shown to give high accuracies in Hindustani raaga recognition are tested in a preliminary experiment to automatically recognize Carnatic raagas. The results are discussed and additional challenges in dealing with melodies in Carnatic tradition are highlighted.

REM: a ray exploration model that caters to the search needs of multi-attribute data

User recall patterns related to multi-attribute data match paradigms more suited to facet browsing than keyword-based search. Canonical keyword-based search methodologies put an undue cognitive load on the user in requiring that the user phrase his/her thoughts into keywords to be used as input for such approaches. Facet classification has been an active paradigm in recent times in enabling exploration of huge catalogs. But even after a considerable effort has gone into building sophisticated and usable models, catalog exploration has been a distant dream of the novice user. We present here a novel approach called Ray Exploration Model (REM) for creating such an interface. REM draws the best from and builds upon connected graphs and facet navigation, thus overcoming the inherent limitations in current interfaces using facet navigation. It conceptualizes the depth of each attribute in the data and breadth across the attributes in an easily navigable sun model with its rays. Together suns and rays form a network using relations between the values of attributes. REM is demonstrated using a prototype to show that it caters to search needs of a user without the limitations of the popular keyword based search approaches.

Culture-aware approaches to modeling and description of intonation using multimodal data

Computational approaches that conform to the cultural context are of paramount importance in music information research. The current state-of-the-art 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 culturally-sound navigation space.