Tejaswinee Kelkar

• Madaghiele, Vincenzo; Fasciani, Stefano; Kelkar, Tejaswinee & Erdem, Cagri (2026). Modeling Relations Between Musical Events in Continuous Time with Transformer Models for Live Co-Improvisational Interactions | Zenodo. In Coelho, Guilherme (Eds.), Proceedings of International Conference on AI and Musical Creativity. AI Music Creativity (AIMC). doi: 10.5281/zenodo.22276042. Full text in Research Archive Show summary

  This paper presents a live semi-autonomous system that co-improvises with a live musician using a model learned from the relationship observed in paired recordings of an improvising duo. This responsive system is based on a deep learning approach that models the relations between sequences of events produced by co-playing musicians in continuous time, using transformer models. We present the architecture for simultaneous sequences of sonic events and provide a quantitative evaluation on several representative tasks. Results are compared against a canonical transformer and a multichannel Factor Oracle, the latter being a widely used model for live sequence-based symbolic music generation. We detail a live implementation employing concatenative synthesis and introduce a customization procedure in which the generative model is iteratively retrained on curated, satisfactory sections from sound recordings of actual human-system co-improvisational interactions. This iterative fine-tuning enables the model’s stylistic output to diverge from the original training corpus. Two musical use cases demonstrate the application of this technique.

• Madaghiele, Vincenzo; Fasciani, Stefano; Kelkar, Tejaswinee & Erdem, Cagri (2026). Autonomous Listening-Based Synthesizer Control with Reinforcement Learning for Live Sound Matching. Proceedings of the International Conference on New Interfaces for Musical Expression. ISSN 2220-4792. p. 751–758. doi: 10.5281/zenodo.20784273. Full text in Research Archive Show summary

  In this paper, we build on a previously proposed method for training an artificial agent that performs live control of sound synthesizers, using reinforcement learning and machine-listening to match the sound of a live musician. The agent listens to an incoming live audio stream and attempts to match its sound according to specific audio descriptors by controlling the parameters of an arbitrary synthesizer. We introduce a reinforcement‑learning model with a continuous action space that integrates sound synthesis during training and live performance. We propose five complementary reward functions for matching target sounds, and evaluate their effectiveness in three scenarios: in‑domain matching of synthesized target sounds, out‑of‑domain matching of synthesized targets, and out‑of‑domain matching using a corpus of musician‑recorded audio. Finally, we compare the proposed reward functions across these scenarios, we discuss a live implementation and we present the results of quantitative experiments evaluating the matching task.

• Arnim, Hugh Alexander von; Christodoulou, Anna-Maria; Burnim, Kayla; Upham, Finn; Kelkar, Tejaswinee & Jensenius, Alexander Refsum (2026). LightHearted—A Framework for Mapping ECG Signals to Light Parameters in Performing Arts. In Brooks, Anthony L. (Eds.), Proceedings of the 14th EAI International Conference on ArtsIT, Interactivity and Game Creation, ArtsIT 2025. Springer. ISSN 9783032269997. p. 719–738. doi: 10.1007/978-3-032-26999-7_18. Full text in Research Archive Show summary

  This paper presents LightHearted, an open-source Python framework for mapping the heart’s electrical activity from electrocardiography (ECG) signals acquired from performers to stage lighting in a concert setting. The aim is to provide dynamic lighting coupled to the physiological processes occurring during performance. Drawing upon critical approaches towards using biosignals in interactive systems and live concert research, we outline the framework’s design and implementation and present a case study with the Aarhus Symphony Orchestra. This paper highlights both technical and conceptual challenges of integrating biosensor-based lighting into a large-scale orchestral context. Results from a post-concert audience survey (N = 324) suggest that while responses to the lighting were mixed, it was generally not perceived as distracting and would be welcomed for use in future concerts.

• Madaghiele, Vincenzo; Fasciani, Stefano; Kelkar, Tejaswinee & Erdem, Cagri (2025). MAAL: a multi-agent autonomous live looper for improvised co-creation of musical structures. In Bown, Oliver (Eds.), Proceedings of International Conference on AI and Musical Creativity. AI Music Creativity (AIMC). doi: 10.5281/ZENODO.16946080. Full text in Research Archive Show summary

  Live looping is a musical practice based on recording, repetition and layering segments of a live music performance. Layering loops is a technique used to create musical structures that evolve over time throughout a performance, and it is particularly popular among solo improvising musicians. This paper presents the Multi-Agent Autonomous Looper -- a system that autonomously selects musical segments to be sampled and looped during a live performance, using a set of rule-based agents. Each loop track is managed by an agent that listens to the musician's performance and to the concurrently looping segments on other tracks. The decision on which segments of the performance to loop depends on user-selected rules based on comparison of musical properties, expressing varying degrees of sonic compatibility. This approach characterizes the entire system as a multi-agent network capable of autonomously generating emergent musical structures throughout a performance. The Multi-Agent Autonomous Looper aims to expand the possibilities for indirect control and co-creative interactions in live looping for improvising musicians. In the paper, we detail the system's objectives and design, its offline and online implementations, and discuss specific settings and behaviors across three representative use cases.

• Arnim, Hugh Alexander von; Kelkar, Tejaswinee & Noven, Live (2025). Motion Pointillism: The (Re/De)Construction of the Normative Body through Motion Capture. Documenta: tijdschrift voor theater. ISSN 0771-8640. 42(1), p. 51–79. doi: 10.21825/documenta.93271. Full text in Research Archive Show summary

  Marker-based, optical motion capture systems make use of reflective markers, interpreting them as clusters of dimensionless points in space. Before labeling and arranging these markers, potentially to fit a model of a kinematic chain, these markers possess little referentiality to objects in physical space. However, the construction of a kinematic model of the human body requires making several assumptions about the body and its affordances. In this article, we problematize the use of the kinematic model in dance performance that employs motion capture, placing focus on the referentiality of visual representations derived from markers and models while examining how motion capture contributes to the construction of the body through the embedding of assumptions and values about what a body is and can do within the technology. Through the design and conceptualization of two interactive dance performances titled Reconfigurations and The Shapeshifter, we develop an approach to working with motion capture that we term motion pointillism, which aims to resist the systemic assumptions embedded in the modeling process. This approach conceptualizes the emergence of the dimensionless points’ referentiality to a human body as a collaborative component of system development and performance, which occurs both in the design of visual representations as well as in the viewers’ perception.

• Memis, Ahmet Emin & Kelkar, Tejaswinee (2024). IEMI: An Immersive, Evolving Motion-Controlled Installation. Proceedings of the SMC Conferences. Full text in Research Archive Show summary

  Interactive Evolutionary Algorithms (IEA) are an extension of Evolutionary Algorithms, where the optimization process is centered around a persons subjective evaluation. IEAs add another layer of user interaction to transform the evolutionary process from being solely dependent on objective selection to incorporating real-time subjective evaluations. This project presents an interactive installation that allows users to engage with a sound bank by moving in space, where the mapping is updated using an evolutionary algorithm. Interactive feedback from the user is captured through button press. In this paper we present a two-stage iterative evaluation of the system. First, a pilot user study was conducted which questions the system’s usability and motion capture quality, as well as making qualitative measurements. The results suggest that the system provides a new and engaging way for users to interact with their space. The system created in this paper is source open and available in a GitHub repository.

• Patel-Grosz, Pritty; Grosz, Patrick Georg; Kelkar, Tejaswinee & Jensenius, Alexander Refsum (2022). Steps towards a semantics of dance. Journal of Semantics. ISSN 0167-5133. 39(4), p. 693–748. doi: 10.1093/jos/ffac009. Full text in Research Archive Show summary

  Abstract As formal theoretical linguistic methodology has matured, recent years have seen the advent of applying it to objects of study that transcend language, e.g., to the syntax and semantics of music (Lerdahl & Jackendoff 1983, Schlenker 2017a; see also Rebuschat et al. 2011). One of the aims of such extensions is to shed new light on how meaning is construed in a range of communicative systems. In this paper, we approach this goal by looking at narrative dance in the form of Bharatanatyam. We argue that a semantic approach to dance can be modeled closely after the formal semantics of visual narrative proposed by Abusch (2013, 2014, 2021). A central conclusion is that dance not only shares properties of other fundamentally human means of expression, such as visual narrative and music, but that it also exhibits similarities to sign languages and the gestures of non-signers (see, e.g., Schlenker 2020) in that it uses space to track individuals in a narrative and performatively portray the actions of those individuals. From the perspective of general human cognition, these conclusions corroborate the idea that linguistic investigations beyond language (see Patel-Grosz et al. forthcoming) can yield insights into the very nature of the human mind and of the communicative devices that it avails.

• Patel-Grosz, Pritty; Katz, Jonah; Grosz, Patrick Georg; Kelkar, Tejaswinee & Jensenius, Alexander Refsum (2022). From music to dance: The inheritance of semantic inferences. Empirical Issues in Syntax and Semantics. 14, p. 219–238. Full text in Research Archive
• Laeng, Bruno; Kuyateh, Sarjo & Kelkar, Tejaswinee (2021). Substituting facial movements in singers changes the sounds of musical intervals. Scientific Reports. 11(1). doi: 10.1038/s41598-021-01797-z. Full text in Research Archive Show summary

  Cross-modal integration is ubiquitous within perception and, in humans, the McGurk effect demonstrates that seeing a person articulating speech can change what we hear into a new auditory percept. It remains unclear whether cross-modal integration of sight and sound generalizes to other visible vocal articulations like those made by singers. We surmise that perceptual integrative effects should involve music deeply, since there is ample indeterminacy and variability in its auditory signals. We show that switching videos of sung musical intervals changes systematically the estimated distance between two notes of a musical interval so that pairing the video of a smaller sung interval to a relatively larger auditory led to compression effects on rated intervals, whereas the reverse led to a stretching effect. In addition, after seeing a visually switched video of an equally-tempered sung interval and then hearing the same interval played on the piano, the two intervals were judged often different though they differed only in instrument. These findings reveal spontaneous, cross-modal, integration of vocal sounds and clearly indicate that strong integration of sound and sight can occur beyond the articulations of natural speech.

• Kelkar, Tejaswinee (2021). Body Data for Music Information Retrieval Tasks. In Wieczorkowska, Alicja & Tsumoto, Shusaku (Ed.), Recommender Systems for Medicine and Music. Springer Nature Switzerland AG 2021. ISSN 9783030664480. p. 153–165. doi: 10.1007/978-3-030-66450-3_10. Full text in Research Archive
• Kelkar, Tejaswinee & Gerry, Lynda Joy (2019). Resonance improviser: A system for transmitting the embodied sensations of vocalization between two people during improvisation. Proceedings of the SMC Conferences. p. 495–496. Full text in Research Archive
• Patel-Grosz, Pritty; Grosz, Patrick Georg; Kelkar, Tejaswinee & Jensenius, Alexander Refsum (2018). Coreference and disjoint reference in the semantics of narrative dance. In Sauerland, Uli & Solt, Stephanie (Ed.), Proceedings of Sinn und Bedeutung 22, vol. 2, ZASPiL 61. Leibniz-Zentrum Allgemeine Sprachwissenschaft (ZAS). p. 199–216. Full text in Research Archive
• Kelkar, Tejaswinee; Roy, Udit & Jensenius, Alexander Refsum (2018). Evaluating a collection of Sound-Tracing Data of Melodic Phrases. In Gómez, Emilia; Hu, Xiao; Humphrey, Eric & Benetos, Emmanouil (Ed.), Proceedings of the 19th International Society for Music Information Retrieval Conference. Institut de Recherche et Coordination Acoustique/Musique. ISSN 9782954035123. Full text in Research Archive Show summary

  Melodic contour, the ‘shape’ of a melody, is a common way to visualize and remember a musical piece. The purpose of this paper is to explore the building blocks of a future ‘gesture-based’ melody retrieval system. We present a dataset containing 16 melodic phrases from four musical styles and with a large range of contour variability. This is accompanied by full-body motion capture data of 26 participants performing sound-tracing to the melodies. The dataset is analyzed using canonical correlation analysis (CCA), and its neural network variant (Deep CCA), to understand how melodic contours and sound tracings relate to each other. The analyses reveal non-linear relationships between sound and motion. The link between pitch and verticality does not appear strong enough for complex melodies. We also find that descending melodic contours have the least correlation with tracing.

• Kelkar, Tejaswinee & Jensenius, Alexander Refsum (2018). Analyzing Free-Hand Sound-Tracings of Melodic Phrases. Applied Sciences. 8(1), p. 1–21. doi: 10.3390/app8010135. Full text in Research Archive Show summary

  In this paper, we report on a free-hand motion capture study in which 32 participants ‘traced’ 16 melodic vocal phrases with their hands in the air in two experimental conditions. Melodic contours are often thought of as correlated with vertical movement (up and down) in time, and this was also our initial expectation. We did find an arch shape for most of the tracings, although this did not correspond directly to the melodic contours. Furthermore, representation of pitch in the vertical dimension was but one of a diverse range of movement strategies used to trace the melodies. Six different mapping strategies were observed, and these strategies have been quantified and statistically tested. The conclusion is that metaphorical representation is much more common than a ‘graph-like’ rendering for such a melodic sound-tracing task. Other findings include a clear gender difference for some of the tracing strategies and an unexpected representation of melodies in terms of a small object for some of the Hindustani music examples. The data also show a tendency of participants moving within a shared ‘social box’.

• Kelkar, Tejaswinee & Jensenius, Alexander Refsum (2017). Representation Strategies in Two-handed Melodic Sound-Tracing. In Gillies, Marco (Eds.), Proceedings of the 4th International Conference on Movement Computing. ACM Publications. ISSN 9781450352093. doi: 10.1145/3077981.3078050. Full text in Research Archive Show summary

  This paper describes an experiment in which the subjects performed a sound-tracing task to vocal melodies. They could move freely in the air with two hands, and their motion was captured using an infrared, marker-based system. We present a typology of distinct strategies used by the recruited participants to represent their perception of the melodies. These strategies appear as ways to represent time and space through the finite motion possibilities of two hands moving freely in space. We observe these strategies and present their typology through qualitative analysis. Then we numerically verify the consistency of these strategies by conducting tests of significance between labeled and random samples.

• Kelkar, Tejaswinee & Jensenius, Alexander Refsum (2017). Exploring melody and motion features in “sound-tracings”. In Lokki, Tapio; Pätynen, Jukka & Välimäki, Vesa (Ed.), Proceedings of the 14th Sound and Music Computing Conference 2017. Aalto University. ISSN 9789526037295. p. 98–103. Full text in Research Archive Show summary

  Pitch and spatial height are often associated when describing music. In this paper we present results from a sound tracing study in which we investigate such sound–motion relationships. The subjects were asked to move as if they were creating the melodies they heard, and their motion was captured with an infra-red, marker-based camera system. The analysis is focused on calculating feature vectors typically used for melodic contour analysis. We use these features to compare melodic contour typologies with motion contour typologies. This is based on using proposed feature sets that were made for melodic contour similarity measurement. We apply these features to both the melodies and the motion contours to establish whether there is a correspondence between the two, and find the features that match the most. We find a relationship between vertical motion and pitch contour when evaluated through features rather than simply comparing contours.

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• Arnim, Hugh Alexander von; Christodoulou, Anna-Maria; Burnim, Kayla; Upham, Finn; Kelkar, Tejaswinee & Jensenius, Alexander Refsum (2025). LightHearted—A Framework for Mapping ECG Signals to Light Parameters in Performing Arts. Full text in Research Archive
• Wærstad, Bernt Isak Grave & Kelkar, Tejaswinee (2024). Three is a crowd? Full text in Research Archive
• Kelkar, Tejaswinee (2024). Household Objects. Full text in Research Archive
• Wærstad, Bernt Isak Grave & Kelkar, Tejaswinee (2024). Three is a Crowd? Full text in Research Archive
• Arnim, Hugh Alexander von & Kelkar, Tejaswinee (2024). The Shapeshifter: Motion Capture and Interactive Dance for Co-constructing the Body. Full text in Research Archive
• Memis, Ahmet Emin & Kelkar, Tejaswinee (2024). IEMI: AN IMMERSIVE, EVOLVING MOTION-CONTROLLED INSTALLATION. Full text in Research Archive
• Kelkar, Tejaswinee (2024). Computational and Multimodal Music Creativity. Full text in Research Archive
• Kelkar, Tejaswinee (2024). Musikkteknologi – skap fremtidens lydbilde. Full text in Research Archive
• Kelkar, Tejaswinee (2024). Nyege Nyege Remix Oslo. Full text in Research Archive
• Kelkar, Tejaswinee (2024). Raga Bad. Full text in Research Archive
• Kelkar, Tejaswinee (2024). Studying Melodic Embodiment: Computational Approaches. Full text in Research Archive
• Kelkar, Tejaswinee (2024). Mira Thiruchelvam & VJNE - Nattjazz. Full text in Research Archive
• Kelkar, Tejaswinee (2024). Marianna Sangitas Gjestehus. Full text in Research Archive
• Kelkar, Tejaswinee & Roshauw, Filip (2024). Ni former for Juice. [Journal]. Jazznytt. Full text in Research Archive
• Kelkar, Tejaswinee (2024). RNNs and Hindustani music. Full text in Research Archive
• Jensenius, Alexander Refsum; Vo, Synne; Kelkar, Tejaswinee & Kjus, Yngvar (2024). Musikksnakk: Musikk på Spotify - hvordan funker algoritmene? Full text in Research Archive Show summary

  Hvorfor er det slik at plateselskaper ønsker at artister skal lage TikTok´er for å promotere musikken sin? Hva bestemmer hvilke musikkanbefalinger du får i Spotify? Og hvordan bruker plateselskapene dataene dine til å generere klikk og lytt? Bli med på en samtale om algoritmer på apper som TikTok og Spotify - og hvordan de påvirker musikksmaken din! Til å diskutere dette kommer: - Synne Vo. Hun er en artist som slo igjennom på TikTok, og bruker plattformen aktivt for å promotere musikken sin. Hun kommer til panelet for å dele sine erfaringer med bransjen og appene. - Yngvar Kjus. Han er professor i musikk og medier på UiO, og har forsket mye på populærmusikk, musikkproduksjon og musikkbransjen. - Tejaswinee Kelkar. Hun er er en sanger og forsker innen musikk og bevegelse. Hun har tidligere jobbet som dataanalytiker i Universal Music Norway og ved RITMO Center of Excellence ved Universitetet i Oslo. Samtalen ledes av Alexander Refsum Jensenius. Han er professor i musikk ved Universitetet i Oslo, og leder av RITMO - Senter for tverrfaglig forskning på rytme, tid og bevegelse. Han prøver hele tiden å forstå mer om hvordan og hvorfor mennesker beveger seg til musikk.

• Kelkar, Tejaswinee (2024). C2HO Workshop on Live Coding with Sonic Pi. Full text in Research Archive
• Kelkar, Tejaswinee (2024). Musical Heritage: Sound Installations and Podcasts with KHM. Full text in Research Archive
• Kelkar, Tejaswinee (2023). Mira Thiruchelvam og Vestnorsk Jazzensemble: Pantone 448 C with Cumin. Full text in Research Archive
• Kelkar, Tejaswinee (2023). Arv - Vår plass i historien. [Internet]. https://www.historiskmuseum.no/utstillinger/arv/podkast/. Full text in Research Archive
• Kelkar, Tejaswinee (2023). Studying melodic embodiment: computational approaches. Full text in Research Archive
• Kelkar, Tejaswinee (2023). Arv - vår plass i historien. Full text in Research Archive
• Kelkar, Tejaswinee (2023). Keynote Speech: Computational Music Creativity. Full text in Research Archive
• Patel-Grosz, Pritty; Katz, Jonah; Grosz, Patrick Georg; Kelkar, Tejaswinee & Jensenius, Alexander Refsum (2021). From music to dance: the inheritance of semantic inferences. Full text in Research Archive
• Kelkar, Tejaswinee & Erdem, Cagri (2021). Two-Body Problem. Full text in Research Archive
• Kelkar, Tejaswinee; Wærstad, Bernt Isak; Susdorf, Marek Karol & Sørbø, Solveig (2021). Solveigs Speisa Musikk | Musikkfest 2021. [Radio]. Oslo, Radio Orakel. Full text in Research Archive
• Kelkar, Tejaswinee (2020). NTT: Noen forsker på musikk helt på ekte - Jazznytt. Full text in Research Archive
• Kelkar, Tejaswinee; Erdem, Cagri & Lan, Qichao (2019). Utopia. Full text in Research Archive
• Kelkar, Tejaswinee (2019). Improverk: Chicago Connections + Kelkar & Shrestha. Full text in Research Archive
• Kelkar, Tejaswinee (2019). Girl Music Tech Camp. Full text in Research Archive Show summary

  Vi inviterer elever fra videregående skole til å lære mer om musikkteknologi. Du får muligheten til å teste ulike instrumenter og synth'er, vil lære å programmere dine egne lyder, og forstå noen av prinsippene som ligger til grunn for digital lyd og musikk. Kurset avholdes hver dag i høstferien fra kl 10-14, og vi vil gå gjennom forskjellige temaer: Lydopptak og redigering Lydanalyse Lydmekking Lydprogrammering Sammenstilling av det hele Planen er å jobbe så praktisk som mulig. Vi legger opp til jam-sessions underveis, samt mulighet for lydopptak med ulike verktøy. Den siste dagen blir det en liten konsert hvor vi setter alt sammen. I løpet av uken får du møtt andre jenter som er opptatt av musikkteknologi, og får snakket med studenter ved Institutt for musikkvitenskap. Du vil også få omvisning i studioer og lab'er og høre litt om den spennende forskningen som foregår ved instituttet.

• Kelkar, Tejaswinee (2019). Spatial and Bodily Metaphors as means for Musical Sense-making. Full text in Research Archive
• Kelkar, Tejaswinee (2019). WoNoMute LittleBits Workshop. Full text in Research Archive
• Kelkar, Tejaswinee (2018). Data visualization in Python. Full text in Research Archive
• Kelkar, Tejaswinee (2018). MAM - Nepal / Avatar. Full text in Research Archive
• Kelkar, Tejaswinee (2018). Jazz in Khartoum - ft Tejaswinee Kelkar #2. Full text in Research Archive
• Kelkar, Tejaswinee (2018). Jazz in Khartoum - ft. Sudeshna Bhattacharya. Full text in Research Archive
• Kelkar, Tejaswinee (2018). Oslo Jazzfestivalen - ft Jazz in Khartoum ft Agbalagba Dada. Full text in Research Archive
• Kelkar, Tejaswinee (2018). Disembodied Voice. Full text in Research Archive
• Kelkar, Tejaswinee (2018). Python Toolboxes for Mocap Analysis. Full text in Research Archive
• Kelkar, Tejaswinee; Roy, Udit & Jensenius, Alexander Refsum (2018). Evaluating a collection of Sound-Tracing Data of Melodic Phrases. Full text in Research Archive
• Kelkar, Tejaswinee (2017). Jazz in Khartoum ft Tejaswinee Kelkar. Full text in Research Archive
• Jensenius, Alexander Refsum & Kelkar, Tejaswinee (2017). Improvisation for Linnstrument, voice and Mogees. Full text in Research Archive
• Kelkar, Tejaswinee & Jensenius, Alexander Refsum (2017). Exploring melody and motion features in “sound-tracings”. Full text in Research Archive Show summary

  Pitch and spatial height are often associated when describing music. In this paper we present results from a sound tracing study in which we investigate such sound–motion relationships. The subjects were asked to move as if they were creating the melodies they heard, and their motion was captured with an infra-red, marker-based camera system. The analysis is focused on calculating feature vectors typically used for melodic contour analysis. We use these features to compare melodic contour typologies with motion contour typologies. This is based on using proposed feature sets that were made for melodic contour similarity measurement. We apply these features to both the melodies and the motion contours to establish whether there is a correspondence between the two, and find the features that match the most. We find a relationship between vertical motion and pitch contour when evaluated through features rather than simply comparing contours.

• Kelkar, Tejaswinee & Jensenius, Alexander Refsum (2017). Representation Strategies in Two-handed Melodic Sound-Tracing. Full text in Research Archive Show summary

  This paper describes an experiment in which the subjects performed a sound-tracing task to vocal melodies. They could move freely in the air with two hands, and their motion was captured using an infrared, marker-based system. We present a typology of distinct strategies used by the recruited participants to represent their perception of the melodies. These strategies appear as ways to represent time and space through the finite motion possibilities of two hands moving freely in space. We observe these strategies and present their typology through qualitative analysis. Then we numerically verify the consistency of these strategies by conducting tests of significance between labeled and random samples.

• Kelkar, Tejaswinee & Jensenius, Alexander Refsum (2017). Tracing melodies: An observation study of hand movements to vocal phrases from different musical cultures. Full text in Research Archive
• Martin, Charles Patrick; Sanchez, Victor Evaristo Gonzalez; Kelkar, Tejaswinee; Zelechowska, Agata; Berggren, Stig Johan & Hopgood, Christina [Show all 14 contributors for this article] (2017). Ensemble Metatone - Improvised Touchscreen Performance. Full text in Research Archive
• Kelkar, Tejaswinee (2017). Motifs in Melodies. Full text in Research Archive
• Kelkar, Tejaswinee (2017). Historical Performance and the Indian Musical Tradition. Full text in Research Archive Show summary

  The influence of musicology on the western classical performance tradition, is in no small part due to the practice of historical performance. This includes reproducing music from a different time period. The reproduction of instruments, interpretation of the way in which the instruments might have been played from text and description, analysis of intonation, the meaning of the poetry from the time period and other areas are involved in the way that performance itself is constructed and taught. The Indian musical tradition relies on a different interpretation of historical performance. Although seminal works are written about Indian music, it continues to be an oral tradition, by virtue of which music itself hasn’t been written down. Philosophy and analysis of music, though, can be found in these seminal works. Stylistic specialization in this style is thus is horizontal, rather than vertical in time –a particular form (khyal or dhrupad), or with the stylistic affliction of one gharana (school) over another is studied deeply, as opposed to specializing in different time periods. Music itself is assumed to be coming from an imagined time in ‘history’ without a date or a place. The qualities of a raga performance or other musical features can be explained by a seminal texts belonging to any time period in history although several things have changed in the pedagogy and performance of music, from use of technology for music making as well as pedagogy. Musicology in this context takes a very different position. The role of the musicologist as an interpreter of performance style in terms of horizontal influence on vocalization and modal content becomes important. This paper will talk about the nature of the interaction of the musicologist and the performer as key figures in the interpretation of the body of Indian music.

• Kelkar, Tejaswinee (2017). Cliche Raga A vocal sketch + neural network look at the Thumri songform. Full text in Research Archive
• Kelkar, Tejaswinee (2016). Tabla performance at Eastern Daze III. Full text in Research Archive
• Kelkar, Tejaswinee (2015). Generation of Quizzes and Solutions based on ontology - a Case for a Music Problem Generator. Full text in Research Archive
• Kelkar, Tejaswinee (2019). Computational Analysis of Melodic Contour and Body Movement. Universitetet i Oslo. Full text in Research Archive Show summary

  The aim of this dissertation is to understand the role of embodiment in melodic contour perception. In other words, it studies how we move our bodies in response to music. Melodies play an important role in both speech and music. This thesis consists of two parts, the first part being a background section discussing the theoretical motivations and methods used. The second part is a collection of four articles. Each of the articles explores a dimension of melodic contour: verticality, motion metaphors, body use, and multi-feature correlational analysis. The empirical work is based on sound-tracing as an experimental paradigm. This brings together the multimodal mappings of pitched sound, gestural imagery evoked by these sounds, and defining geometries of these contours. Two sound-tracing experiments were conducted, resulting in three datasets that have been used in the analyses. In the experiments, participants listened to 16 melodies from four different genres: operatic vocalise, jazz scatting, North-Indian singing, and Sámi joik. The participants listened to each melody twice, the first time standing still, and the second time ``drawing'' the sounds in the air. Infrared motion capture was used to record the participants' body movement, and the analysis is focused primarily on the movement of their hands. The sound analysis is based on signal processing algorithms for pitch detection and methods for contour representation. Cross-correlation of the data is performed using a range of methods from statistical hypothesis testing to canonical correlation analysis. The analysis reveals that although there is a natural propensity to describe pitches in terms of the vertical dimension, the experimental data do not clearly show such an association. Average profiles of movement responses to melodies have an arch-like representation, regardless of the contour of the melody. Spatial height is associated more with relative pitch in the melodic context traced, rather than an absolute pitch scale. In addition, movement indicating metaphoric representation of sound was used more often by participants.

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