Madaghiele, V., Lund, L., Holzer, D., Kelkar, T., Tatar, K., and Holzapfel, A. 2026. Expanding the machine: Notating generative synthesis with a state-based representation and a navigable timbre space. Organised Sound. DOI:10.1017/S1355771825100915.
LinkPublications
Journal Publications
Imploding between the facts and concerns: analysing human–AI musical interaction
Cotton, K., Kaila, A., Jääskeläinen, P., Holzapfel, A., and Tatar, K. 2025. Imploding between the facts and concerns: analysing human–AI musical interaction. Humanities and Social Sciences Communications. DOI:10.1057/s41599-025-04533-4.
LinkZappi, V. and Tatar, K. 2025. Neural audio instruments: epistemological and phenomenological perspectives on musical embodiment of deep learning. Frontiers in Computer Science. DOI:10.3389/fcomp.2025.1575168.
LinkA Shift in Artistic Practices through Artificial Intelligence
Tatar, K., Ericson, P., Cotton, K., Del Prado, P. T. N., Batlle-Roca, R., Cabrero-Daniel, B., Ljungblad, S., Diapoulis, G., and Hussain, J. 2024. A Shift in Artistic Practices through Artificial Intelligence. Leonardo. DOI:10.1162/leon_a_02523.
Linkinstance: Soma-based multi-user interaction design for the telematic sonic arts
Strauss, L., Tatar, K., and Nuro, S. 2021. instance: Soma-based multi-user interaction design for the telematic sonic arts. Organised Sound. DOI:10.1017/S1355771821000479.
LinkTatar, K., Bisig, D., and Pasquier, P. 2020. Latent Timbre Synthesis. Neural Computing and Applications. DOI:10.1007/s00521-020-05424-2.
LinkInitial Remarks on Analyzing Acousmatic Music from the Perspective of Multi-agents
Tatar, K. 2019. Initial Remarks on Analyzing Acousmatic Music from the Perspective of Multi-agents. Array. DOI:http://dx.doi.org/10.25532/OPARA-46.
LinkMusical agents: A typology and state of the art towards Musical Metacreation
Tatar, K. and Pasquier, P. 2019. Musical agents: A typology and state of the art towards Musical Metacreation. Journal of New Music Research. DOI:10.1080/09298215.2018.1511736.
LinkRespire: Virtual Reality Art with Musical Agent Guided by Respiratory Interaction
Tatar, K., Prpa, M., and Pasquier, P. 2019. Respire: Virtual Reality Art with Musical Agent Guided by Respiratory Interaction. Leonardo Music Journal. DOI:10.1162/lmj_a_01057.
LinkReview: Concert No. 12
Tatar, K. 2019. Review: Concert No. 12. Array.
Automatic Synthesizer Preset Generation with PresetGen
Tatar, K., Macret, M., and Pasquier, P. 2016. Automatic Synthesizer Preset Generation with PresetGen. Journal of New Music Research. DOI:10.1080/09298215.2016.1175481.
LinkConference Publications
A Deep Learning Framework for Musical Acoustics Simulations
Chen, J., Tatar, K., and Zappi, V. 2024. A Deep Learning Framework for Musical Acoustics Simulations. In Proceedings of the {AI} {Music} {Creativity} {Conference} 2024.
LinkInterfacing ErgoJr with Creative Coding Platforms
Caravati, M. and Tatar, K. 2024. Interfacing ErgoJr with Creative Coding Platforms. In Proceedings of the 9th {International} {Conference} on {Movement} and {Computing}. DOI:10.1145/3658852.3659082.
LinkSinging for the Missing: Bringing the Body Back to AI Voice and Speech Technologies
Cotton, K., De Vries, K., and Tatar, K. 2024. Singing for the Missing: Bringing the Body Back to AI Voice and Speech Technologies. In Proceedings of the 9th {International} {Conference} on {Movement} and {Computing}. DOI:10.1145/3658852.3659065.
LinkCotton, K. and Tatar, K. 2024. Sounding out extra-normal AI voice: Non-normative musical engagements with normative AI voice and speech technologies. In Proceedings of the {AI} {Music} {Creativity} {Conference} 2024.
LinkCaring Trouble and Musical AI: Considerations towards a Feminist Musical AI
Cotton, K. and Tatar, K. 2023. Caring Trouble and Musical AI: Considerations towards a Feminist Musical AI. In {AIMC} 2023.
LinkSound Design Strategies for Latent Audio Space Explorations Using Deep Learning Architectures
Tatar, K., Cotton, K., and Bisig, D. 2023. Sound Design Strategies for Latent Audio Space Explorations Using Deep Learning Architectures.
Raw Music from Free Movements: Early Experiments in Using Machine Learning to Create Raw Audio from Dance Movements
Bisig, D. and Tatar, K. 2021. Raw Music from Free Movements: Early Experiments in Using Machine Learning to Create Raw Audio from Dance Movements. In Proceedings of the 2nd {AI} {Music} {Creativity} {Conference} ({AIMC} 2021).
Chatterbox: an interactive system of gibberish agents
Boersen, R., Liu-Rosenbaum, A., Tatar, K., and Pasquier, P. 2020. Chatterbox: an interactive system of gibberish agents. In Proceedings of 26th {International} {Symposium} of {Electronic} {Arts} ({ISEA} 2020).
LinkAudio-based Musical Artificial Intelligence and Audio-Reactive Visual Agents in Revive
Tatar, K., Pasquier, P., and Siu, R. 2019. Audio-based Musical Artificial Intelligence and Audio-Reactive Visual Agents in Revive. In Proceedings of the joint {International} {Computer} {Music} {Conference} and {New} {York} {City} {Electroacoustic} {Music} {Festival} 2019 ({ICMC}-{NYCEMF} 2019).
Prpa, M., Tatar, K., Françoise, J., Riecke, B., Schiphorst, T., and Pasquier, P. 2018. Attending to Breath: Exploring How the Cues in a Virtual Environment Guide the Attention to Breath and Shape the Quality of Experience to Support Mindfulness. In Proceedings of the 2018 {Designing} {Interactive} {Systems} {Conference}. DOI:10.1145/3196709.3196765.
LinkQuantitative Analysis of the Impact of Mixing on Perceived Emotion of Soundscape Recordings
Fan, J., Thorogood, M., Tatar, K., and Pasquier, P. 2018. Quantitative Analysis of the Impact of Mixing on Perceived Emotion of Soundscape Recordings. In Proceedings of the 15th {Sound} and {Music} {Computing} {Conference} ({SMC2018}). DOI:10.5281/zenodo.1408596.
LinkRespire: a Breath Away from the Experience in Virtual Environment
Prpa, M., Schiphorst, T., Tatar, K., and Pasquier, P. 2018. Respire: a Breath Away from the Experience in Virtual Environment. In {CHI} {EA} '18 {Extended} {Abstracts} of the 2018 {CHI} {Conference} on {Human} {Factors} in {Computing} {Systems}. DOI:10.1145/3170427.3180282.
LinkREVIVE: An Audio-visual Performance with Musical and Visual AI Agents
Tatar, K., Pasquier, P., and Siu, R. 2018. REVIVE: An Audio-visual Performance with Musical and Visual AI Agents. In {CHI} {EA} '18 {Extended} {Abstracts} of the 2018 {CHI} {Conference} on {Human} {Factors} in {Computing} {Systems}. DOI:10.1145/3170427.3177771.
LinkMASOM: A Musical Agent Architecture based on Self Organizing Maps, Affective Computing, and Variable Markov Models
Tatar, K. and Pasquier, P. 2017. MASOM: A Musical Agent Architecture based on Self Organizing Maps, Affective Computing, and Variable Markov Models. In Proceedings of the 5th {International} {Workshop} on {Musical} {Metacreation} ({MUME} 2017).
Ranking-Based Emotion Recognition for Experimental Music
Fan, J., Tatar, K., Thorogood, M., and Pasquier, P. 2017. Ranking-Based Emotion Recognition for Experimental Music. In Proceedings of the {International} {Symposium} on {Music} {Information} {Retrieval} ({ISMIR}) 2017.
Prpa, M., Tatar, K., Riecke, B. E., and Pasquier, P. 2017. The Pulse Breath Water System: Exploring Breathing as an Embodied Interaction for Enhancing the Affective Potential of Virtual Reality. In Virtual, {Augmented} and {Mixed} {Reality}, 9th {International} {Conference}, {VAMR} 2017, {Held} as {Part} of {HCI} {International} 2017, {Proceedings}.
LinkThesis
Musical agents based on self-organizing maps for audio applications
Tatar, K. 2019. Musical agents based on self-organizing maps for audio applications. Communication, Art \& Technology: School of Interactive Arts and Technology.
LinkOther Publications
ARIA: A Diagnostic Framework for Music Training Data Attribution
Han, C., Panahi, A., and Tatar, K. 2026. ARIA: A Diagnostic Framework for Music Training Data Attribution. arXiv. arXiv:2605.16181 [cs.SD]. DOI:10.48550/arXiv.2605.16181.
LinkLiu, H. X. and Tatar, K. 2026. Grounding Machine Creativity in Game Design Knowledge Representations: Empirical Probing of LLM-Based Executable Synthesis of Goal Playable Patterns under Structural Constraints. arXiv. arXiv:2603.07101 [cs]. DOI:10.48550/arXiv.2603.07101.
LinkMage: Multi-Axis Evaluation of LLM-Generated Executable Game Scenes Beyond Compile-Pass Rate
Liu, H. X. and Tatar, K. 2026. Mage: Multi-Axis Evaluation of LLM-Generated Executable Game Scenes Beyond Compile-Pass Rate. arXiv. arXiv:2605.07342 [cs.LG]. DOI:10.48550/arXiv.2605.07342.
LinkExpert Procrastinator's Tool: Artificial Intelligence
Unknown author 2023. Expert Procrastinator's Tool: Artificial Intelligence.
LinkOn the importance of AI research beyond disciplines
Dignum, V., Casey, D., Cerratto-Pargman, T., Dignum, F., Fantasia, V., Formark, B., Hammarfelt, B., Holmberg, G., Holzapfel, A., Larsson, S., Lagerkvist, A., Lakemond, N., Lindgren, H., Lorig, F., Marusic, A., Rahm, L., Razmetaeva, Y., Sikström, S., Tatar, K., and Tucker, J. 2023. On the importance of AI research beyond disciplines. arXiv. arXiv:2302.06655 [cs]. DOI:10.48550/arXiv.2302.06655.
LinkIntroducing Latent Timbre Synthesis
Tatar, K., Bisig, D., and Pasquier, P. 2020. Introducing Latent Timbre Synthesis. \_eprint: 2006.00408. \_eprint: 2006.00408.
LinkLatent Timbre Synthesis — Kıvanç Tatar \textbar Creative Artificial Intelligence
Tatar, K. n.d. Latent Timbre Synthesis — Kıvanç Tatar \textbar Creative Artificial Intelligence.
Link