Theorizing Trikāla: A Generalized Intervallic Approach to Pulse Transformation in South Indian Carnatic Music

The rich rhythmic/metric construction of South Indian Carnatic music is characterized, in large part, by intricate interplay between an internalized metric cycle called the tāḷa and performed phrases that may generate expressive tension with this tāḷa. Thus, informed listeners, who track the tāḷa using standardized hand gestures (kriyās), may experience substantial internal tension. A common source of such tension is trikāla technique, in which the performed pulse unit expands or contracts over constant tāḷa. While trikāla has been thoroughly described performatively, historically, and culturally, it has received little attention within the music-theoretic realm. Thus, this paper seeks to approach trikāla technique from the perspective of Lewin’s (1987) transformation theory, applying the metric generalized interval system (GIS) Met developed by Wells (2015a; 2015b; 2017) to the problem of representing and quantifying this technique. The first part of this article lays the theoretical foundation for Met-based analysis of Carnatic music, demonstrating basic techniques for representing Carnatic rhythmic/metric structures using the GIS. Of these techniques, the most significant for the current study are intervallic expansion and contraction transformations. Because these transformations are generated by expanding or contracting pulse units over a constant background meter, they are ideally suited for modeling Carnatic trikāla technique. The next two sections apply these theoretical ideas to analyses of traditional pedagogical exercises called alankārams and a rāgam-tānam-pallavi (RTP) performance previously investigated by Widdess (1977). These Met-based analyses reveal hidden aspects of the music’s metric workings, demonstrating how seemingly simple expansions and contractions of the melodic pulse unit can drastically increase rhythmic/metric complexity. Moreover, in the alankaram exercises and the RTP performance, the intervallic developments suggest a striking balance between stability and change in the relationships between melody and tāḷa. The Met intervals also act as analytical agents, representing contextual metric functions and rhythmic/metric sources of musical development. Ultimately, the analyses not only provide new insights into trikāla technique in the specific examples in question, but suggest new possibilities for rhythmic/metric analysis of tāḷa-based Carnatic music more generally

Musicolinguistic artistry of niraval in Carnatic vocal music

Niraval is a form of virtuosic musicolinguistic improvisation in Carnatic music whereby a line within a song is repeated in various melodic and rhythmic manifestations within the rāgam (melodic framework) and tāḷam (beat cycle). For a Carnatic singer, niraval makes different aesthetic demands than other forms of non-textual improvisation within the tradition. To convey artful, sincere renditions of the same lyrical text, the singer-musician must imaginatively devise interesting repetitions which attend to both melodic and rhythmic elements and the lyric text. Combining melodic and rhythmic skill and verbal artistry in a range of South Indian languages as well as Sanskrit, Carnatic singers display extraordinary communicative and artistic competence and captivate their audiences. This paper analyses the musical and linguistic elements of a single niraval performance in Sydney’s Carnatic music community. It is hoped that such research will contribute to a greater understanding of the interplay of language and music in sung performanceANU College of Arts & Social Sciences, School of Language Studies; ANU College of Asia and the Pacific, School of Culture, History and Languag

Thaat Classification Using Recurrent Neural Networks with Long Short-Term Memory and Support Vector Machine

This research paper introduces a groundbreaking method for music classification, emphasizing thaats rather than the conventional raga-centric approach. A comprehensive range of audio features, including amplitude envelope, RMSE, STFT, spectral centroid, MFCC, spectral bandwidth, and zero-crossing rate, is meticulously used to capture thaats' distinct characteristics in Indian classical music. Importantly, the study predicts emotional responses linked with the identified thaats. The dataset encompasses a diverse collection of musical compositions, each representing unique thaats. Three classifier models - RNN-LSTM, SVM, and HMM - undergo thorough training and testing to evaluate their classification performance. Initial findings showcase promising accuracies, with the RNN-LSTM model achieving 85% and SVM performing at 78%. These results highlight the effectiveness of this innovative approach in accurately categorizing music based on thaats and predicting associated emotional responses, providing a fresh perspective on music analysis in Indian classical music

Vocal Source Separation for Carnatic Music

Carnatic Music is a Classical music form that originates from the South of India and is extremely varied from Western genres. Music Information Retrieval (MIR) has predominantly been used to tackle problems in western musical genres and cannot be adapted to non western musical styles like Carnatic Music due to the fundamental difference in melody, rhythm, instrumentation, nature of compositions and improvisations. Due to these conceptual differences emerged MIR tasks specific for the use case of Carnatic Music. Researchers have constantly been using domain knowledge and technology driven ideas to tackle tasks like Melodic analysis, Rhythmic analysis and Structural segmentation. Melodic analysis of Carnatic Music has been a cornerstone in MIR research and heavily relies on the singing voice because the singer offers the main melody. The problem is that the singing voice is not isolated and has melodic, percussion and drone instruments as accompaniment. Separating the singing voice from the accompanying instruments usually comes with issues like bleeding of the accompanying instruments and loss of melodic information. This in turn has an adverse effect on the melodic analysis. The datasets used for Carnatic-MIR are concert recordings of different artistes with accompanying instruments and there is a lack of clean isolated singing voice tracks. Existing Source Separation models are trained extensively on multi-track audio of the rock and pop genre and do not generalize well for the use case of Carnatic music. How do we improve Singing Voice Source Separation for Carnatic Music given the above constraints? In this work, the possible contributions to mitigate the existing issue are ; 1) Creating a dataset of isolated Carnatic music stems. 2) Reusing multi-track audio with bleeding from the Saraga dataset. 3) Retraining and fine tuning existing State of the art Source Separation models. We hope that this effort to improve Source Separation for Carnatic Music can help overcome existing shortcomings and generalize well for Carnatic music datasets in the literature and in turn improve melodic analysis of this music culture

MODELLING GAMAKAS OF CARNATIC MUSIC AS A SYNTHESIZER FOR SPARSE PRESCRIPTIVE NOTATION

Ph.DDOCTOR OF PHILOSOPH

Culturally sensitive strategies for automatic music prediction

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 103-112).Music has been shown to form an essential part of the human experience-every known society engages in music. However, as universal as it may be, music has evolved into a variety of genres, peculiar to particular cultures. In fact people acquire musical skill, understanding, and appreciation specific to the music they have been exposed to. This process of enculturation builds mental structures that form the cognitive basis for musical expectation. In this thesis I argue that in order for machines to perform musical tasks like humans do, in particular to predict music, they need to be subjected to a similar enculturation process by design. This work is grounded in an information theoretic framework that takes cultural context into account. I introduce a measure of musical entropy to analyze the predictability of musical events as a function of prior musical exposure. Then I discuss computational models for music representation that are informed by genre-specific containers for musical elements like notes. Finally I propose a software framework for automatic music prediction. The system extracts a lexicon of melodic, or timbral, and rhythmic primitives from audio, and generates a hierarchical grammar to represent the structure of a particular musical form. To improve prediction accuracy, context can be switched with cultural plug-ins that are designed for specific musical instruments and genres. In listening experiments involving music synthesis a culture-specific design fares significantly better than a culture-agnostic one. Hence my findings support the importance of computational enculturation for automatic music prediction. Furthermore I suggest that in order to sustain and cultivate the diversity of musical traditions around the world it is indispensable that we design culturally sensitive music technology.by Mihir Sarkar.Ph.D

It's Not Fusion: Hybridity in the Music of Vijay Iyer and Rudresh Mahanthappa

This thesis concerns the performance of identity in the music of Indian American jazz musicians Rudresh Mahanthappa and Vijay Iyer. In combining the use of Indian classical music elements with jazz, Iyer and Mahanthappa create music that is inextricably tied to their multifaceted identities. Traditional musicological analysis is juxtaposed with a theoretical framework that draws on postcolonial theory and the history of Asian immigrant populations to the U.S. I chronicle the interactions between Indian and Western music and link it to larger issues of Asian American identity formation and activism through music. Through interviews and transcriptions of studio recordings, I identify specific compositional and improvisational strategies of the musicians. I emphasize the role of individual agency in the formation of second-generation identities, drawing attention to the distinct ways that Iyer and Mahanthappa approach their music. Finally, I connect this research to a larger discourse on Indian American artistic identity