Music Similarity Estimation

Music is a complicated form of communication, where creators and culture communicate and expose their individuality. After music digitalization took place, recommendation systems and other online services have become indispensable in the field of Music Information Retrieval (MIR). To build these systems and recommend the right choice of song to the user, classification of songs is required. In this paper, we propose an approach for finding similarity between music based on mid-level attributes like pitch, midi value corresponding to pitch, interval, contour and duration and applying text based classification techniques. Our system predicts jazz, metal and ragtime for western music. The experiment to predict the genre of music is conducted based on 450 music files and maximum accuracy achieved is 95.8% across different n-grams. We have also analyzed the Indian classical Carnatic music and are classifying them based on its raga. Our system predicts Sankarabharam, Mohanam and Sindhubhairavi ragas. The experiment to predict the raga of the song is conducted based on 95 music files and the maximum accuracy achieved is 90.3% across different n-grams. Performance evaluation is done by using the accuracy score of scikit-learn

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

The margin and the mainstream : positioning Harry Partch's theories within the broader discourse of musical aesthetics

Bibliography: leaves 102-106.The dissertation examines the broader musical value of microtonal composer Harry Partch's musical theories by locating his critique of abstract music within mainstream compositional theory and aesthetics. This contextualisation aims to deconstruct Partch's iconoclastic image so as to understand his contribution within a wider realm of critical discourse. The work of composers that follow in Partch's footsteps becomes important in this context, especially that of his one-time student Ben Johnston whose own microtonal aesthetic is firmly rooted in European aesthetics from Debussy to Schoenberg. By a study of Johnston's utilisation of Partch's theory of just intonation the dissertation attempts to arrive at a more inclusive compositional theory, one which continues to address those aspects of Partch's theories that serve as a valid and constructive critique of traditional musical values. Taking Adorno's view that musical critique must deal with the problem of reification at the level of musical materials, the author proposes a reading of Partch's corporeal philosophy that is applicable beyond the confines of narrative musical drama. By creating a distinction between historical models of organisation and 'second nature' forms of musical presentation, it is suggested that critique does not necessarily prefigure alienation from the mainstream, but can rather be situated within musical discourse in such a way that a new image of the latter's forms results. On a practical level, the dissertation explores the validity of expanded just intonation as a means of achieving this immanent critique, both in the realm of compositional theory and, implicitly, in that of analytical theory, concluding with the description of a tuning system with the capacity to synthesise the range of compositional theories explored

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

Non-Isochronous Meter: A Study of Cross cultural practice, analytic technique, and implications for jazz pedagogy

This dissertation examines the use of non-isochronous (NI) meters in jazz compositional and performative practices (meters as comprised of cycles of a prime number [e.g., 5, 7, 11] or uneven divisions of non-prime cycles [e.g., 9 divided as 2+2+2+3]). The explorative meter practices of jazz, while constituting a central role in the construction of its own identity, remains curiously absent from jazz scholarship. The conjunct research broadly examines NI meters and the various processes/strategies and systems utilized in historical and current jazz composition and performance practices. While a considerable amount of NI meter composers have advertantly drawn from the metric practices of non-Western music traditions, the potential for utilizing insights gleaned from contemporary music-theoretical discussions of meter have yet to fully emerge as a complimentary and/or organizational schemata within jazz pedagogy and discourse. This paper seeks to address this divide, but not before an accurate picture of historical meter practice is assessed, largely as a means for contextualizing developments within historical and contemporary practice and discourse. The dissertation presents a chronology of explorative meter developments in jazz, firstly, by tracing compositional output, and secondly, by establishing the relevant sources within conjunct periods of development i.e., scholarly works, relative academic developments, and tractable world music sources. Bridging the gap between world music meter sources and theoretical musicology (primarily, the underlying perceptual and cognitive model which represents a topology of the structural premises of meter) the research acts to direct and inform a compositional process which directly accounts for an isomorphic link between structurally similar meters

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

Ph.DDOCTOR OF PHILOSOPH

Repertoire-Specific Vocal Pitch Data Generation for Improved Melodic Analysis of Carnatic Music

Deep Learning methods achieve state-of-the-art in many tasks, including vocal pitch extraction. However, these methods rely on the availability of pitch track annotations without errors, which are scarce and expensive to obtain for Carnatic Music. Here we identify the tradition-related challenges and propose tailored solutions to generate a novel, large, and open dataset, the Saraga-Carnatic-Melody-Synth (SCMS), comprising audio mixtures and time-aligned vocal pitch annotations. Through a cross-cultural evaluation leveraging this novel dataset, we show improvements in the performance of Deep Learning vocal pitch extraction methods on Indian Art Music recordings. Additional experiments show that the trained models outperform the currently used heuristic-based pitch extraction solutions for the computational melodic analysis of Carnatic Music and that this improvement leads to better results in the musicologically relevant task of repeated melodic pattern discovery when evaluated using expert annotations. The code and annotations are made available for reproducibility. The novel dataset and trained models are also integrated into the Python package compIAM1 which allows them to be used out-of-the-box