Towards building a Deep Learning based Automated Indian Classical Music Tutor for the Masses

Music can play an important role in the well-being of the world. Indian classical music is unique in its requirement for rigorous, disciplined, expert-led training that typically goes on for years before the learner can reach a reasonable level of performance. This keeps many, including the first author of this paper, away from mastering the skill. The problem is particularly compounded in rural areas, where the available expertise may be limited and prohibitively expensive, but the interest in learning classical music still prevails, nevertheless. Machine Learning has been complementing, enhancing, and replacing many white-collar jobs and we believe it can help with this problem as well. This paper describes efforts at using Machine Learning techniques, particularly, Long Short-Term Memory for building a system that is a step toward provisioning an Indian Classical Music Tutor for the masses. The system is deployed in the cloud using orchestrated containerization for potential worldwide access, load balancing, and other robust features

Le patrimoine musical indien dans le répertoire pour piano d'Olivier Messiaen et de Jacques Charpentier

The Indian music inheritance constitutes a strong source of inspiration and attraction for Western modern and contemporary composers who shaped their personal style by expanding and fusing the Western and Eastern horizon of musical knowledge. In this paper, we will circumscribe our study on the effects the Indian music heritage had over two French composers with a kinship for being teacher and pupil: Olivier Messiaen (Avignon, 1908 - Clichy, 1992) and Jacques Charpentier (Paris 1933). Although both composers were pianists and organists and those works they dedicated to piano and organ are the principal sources of their composing technique, we will focus on such piano repertory, which reveals mainly the influence of Indian music. Our aim is to draw a subsequent connective direct line from Olivier Messiaenâs teachings to Jacques Charpentierâs 72 Etudes Karnatiques pour piano (1957 - 1984), which we consider a significant trace of the Indian culture inheritance in the Western piano contemporary repertory still to be deeply examined. In our discussion, we will overview, at first, the specific historical context by explaining how and why Olivier Messiaenâs Western style roots to the Indian music tradition; secondly, we will retrace the effects and the evolution of his mixed heritage he transmitted to the latest young generations of contemporary composers. Therefore, we considered taking into account one of his pupil most bonded to Indian music, Jacques Charpentier and his extraordinary work for piano based on the 72 musical scales from South India, called Karnatic. Thus, we will set out how the Indian music structures, philosophical and aesthetic ideas biased the rhetorical and symbolic ends of both composers by illustrating their new prospective in treating the Western musical parameters, such as rhythm, melody and harmony, in piano composition. Our overview would offer a useful retrospective in pointing out the strong added value the Indian music inheritance brought on the French piano contemporary repertory while opening new outlooks on the symbolic and philosophic reflection of the cultural inheritance in music composition

Exploring deep learning based methods for information retrieval in Indian classical music

A vital aspect of Indian Classical Music (ICM) is Raga, which serves as a melodic framework for compositions and improvisations alike. Raga Recognition is an important music information retrieval task in ICM as it can aid numerous downstream applications ranging from music recommendations to organizing huge music collections. In this work, we propose a deep learning based approach to Raga recognition. Our approach employs efficient pre-possessing and learns temporal sequences in music data using Long Short Term Memory based Recurrent Neural Networks (LSTM-RNN). We train and test the network on smaller sequences sampled from the original audio while the final inference is performed on the audio as a whole. Our method achieves an accuracy of 88.1% and 97 % during inference on the Comp Music Carnatic dataset and its 10 Raga subset respectively making it the state of-the-art for the Raga recognition task. Our approach also enables sequence ranking which aids us in retrieving melodic patterns from a given music data base that are closely related to the presented query sequence

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

Engineering systematic musicology : methods and services for computational and empirical music research

One of the main research questions of *systematic musicology* is concerned with how people make sense of their musical environment. It is concerned with signification and meaning-formation and relates musical structures to effects of music. These fundamental aspects can be approached from many different directions. One could take a cultural perspective where music is considered a phenomenon of human expression, firmly embedded in tradition. Another approach would be a cognitive perspective, where music is considered as an acoustical signal of which perception involves categorizations linked to representations and learning. A performance perspective where music is the outcome of human interaction is also an equally valid view. To understand a phenomenon combining multiple perspectives often makes sense. The methods employed within each of these approaches turn questions into concrete musicological research projects. It is safe to say that today many of these methods draw upon digital data and tools. Some of those general methods are feature extraction from audio and movement signals, machine learning, classification and statistics. However, the problem is that, very often, the *empirical and computational methods require technical solutions* beyond the skills of researchers that typically have a humanities background. At that point, these researchers need access to specialized technical knowledge to advance their research. My PhD-work should be seen within the context of that tradition. In many respects I adopt a problem-solving attitude to problems that are posed by research in systematic musicology. This work *explores solutions that are relevant for systematic musicology*. It does this by engineering solutions for measurement problems in empirical research and developing research software which facilitates computational research. These solutions are placed in an engineering-humanities plane. The first axis of the plane contrasts *services* with *methods*. Methods *in* systematic musicology propose ways to generate new insights in music related phenomena or contribute to how research can be done. Services *for* systematic musicology, on the other hand, support or automate research tasks which allow to change the scope of research. A shift in scope allows researchers to cope with larger data sets which offers a broader view on the phenomenon. The second axis indicates how important Music Information Retrieval (MIR) techniques are in a solution. MIR-techniques are contrasted with various techniques to support empirical research. My research resulted in a total of thirteen solutions which are placed in this plane. The description of seven of these are bundled in this dissertation. Three fall into the methods category and four in the services category. For example Tarsos presents a method to compare performance practice with theoretical scales on a large scale. SyncSink is an example of a service

Gesture in Karnatak Music: Pedagogy and Musical Structure in South India

This thesis presents an examination of gesture in Karnatak music, the art music of South India. The topic is approached from two perspectives; the first considers Karnatak music structure from a gestural perspective, looking both at the music itself and at the gestures that create it, while the second enquires into the role played by physical gesture in vocal pedagogy. The broader aims of the thesis are to provide insight into the musical structure of the Karnatak style, and to contribute to wider discourses on connections between music and movement. An interdisciplinary approach to the research is taken, drawing on theories and methods from the fields of ethnomusicology, embodied music cognition, and gesture studies. The first part of the thesis opens with a discussion of differences between practical and theoretical conceptions of the Karnatak style. I argue for the significance in practice of svara-gamaka units and longer motifs formed of chains of such units, and also consider the gestural qualities of certain motifs and their contribution to bhāva (mood). Subsequently, I present a joint musical and motoric analysis of a section of Karnatak violin performance, seeking to elucidate the dynamic processes that form the style. The second part of the thesis enquires into the role played by hand gestures produced by teachers and students in vocal lessons, looking at what is indexed by the gestures and how such indexing contributes to the pedagogic process. This part of the thesis also considers how gestures contribute to the formation and maintenance of common ground between teacher and student. The final chapter brings the two strands of this thesis together to discuss the connections that exist between musical and physical gesture in Karnatak music