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.

Expanding the machine: Notating generative synthesis with a state-based representation and a navigable timbre space

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.

Grounding Machine Creativity in Game Design Knowledge Representations: Empirical Probing of LLM-Based Executable Synthesis of Goal Playable Patterns under Structural Constraints

Liu, 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.

Knowledge-Conditioned, Single-Pass LLM Synthesis of Executable Unity Game Scenes: A Compiler Error Census across 26 Goal Playable Concepts

Xuechen Liu, H. and Tatar, K. 2026. Knowledge-Conditioned, Single-Pass LLM Synthesis of Executable Unity Game Scenes: A Compiler Error Census across 26 Goal Playable Concepts. arXiv e-prints.

Mage: 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.

Revising research practices for singing data collection

Cotton, K., Holzapfel, A., de Vries, K., Berglund, K., and Tatar, K. 2026. Revising research practices for singing data collection. AI and Society.

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.

Neural audio instruments: epistemological and phenomenological perspectives on musical embodiment of deep learning

Zappi, 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.

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.

A 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.

Interfacing 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.

Singing 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.

Sounding out extra-normal AI voice: Non-normative musical engagements with normative AI voice and speech technologies

Cotton, 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.

Caring 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.

On 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.

Sound 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. In Proceedings of the Sound and Music Computing 2023 (SMC 2023).

Bottom-up live coding: Analysis of continuous interactions towards predicting programming behaviours

Diapoulis, G., Zannos, I., Tatar, K., and Dahlstedt, P. 2022. Bottom-up live coding: Analysis of continuous interactions towards predicting programming behaviours. In International Conference on New Interfaces for Musical Expression.

Social drones for health and well-being

Obaid, M., Tatar, K., Wiberg, M., Said, A., Rost, M., Weilenmann, A., Johal, W., and Eyssel, F. 2022. Social drones for health and well-being. In Adjunct proceedings of the 2022 nordic human-computer interaction conference.

The Neuralacoustics Project: Exploring Deep-Learnin for Lightweight Numerical Modeling Synthesis

Zappi, V. and Tatar, K. 2022. The Neuralacoustics Project: Exploring Deep-Learnin for Lightweight Numerical Modeling Synthesis. In Embedded AI for NIME: Challenges and Opportunities Workshop at New Interfaces for Musical Expression..

instance: 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.

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 AI Music Creativity Conference 2021 (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).

Introducing Latent Timbre Synthesis

Tatar, K., Bisig, D., and Pasquier, P. 2020. Introducing Latent Timbre Synthesis.

Latent Timbre Synthesis

Tatar, K., Bisig, D., and Pasquier, P. 2020. Latent Timbre Synthesis. Neural Computing and Applications. DOI:10.1007/s00521-020-05424-2.

Audio-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).

Initial 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.

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.

Musical 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.

Respire: 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.

Review: Concert No. 12

Tatar, K. 2019. Review: Concert No. 12. Array.

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

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.

Quantitative 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.

Respire: 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.

REVIVE: 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.

MASOM: 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.

The Pulse Breath Water System: Exploring Breathing as an Embodied Interaction for Enhancing the Affective Potential of Virtual Reality

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.

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.