Kivanc Tatar
Kıvanç Tatar is an artist/technologist/researcher working at the intersection of Music, Machine Learning, Artificial Intelligence, Interactive Arts, Design, and Human-Computer Interaction. His research in musical AI includes multimodal applications that combine music with movement computation, or visual arts. His computational approaches have been integrated into musical performances, interactive artworks, and immersive environments including virtual reality. Tatar’s interdisciplinary work has been exhibited across the globe; including the notable events of the cultural program at Rio Olympics 2016, the Ars Electronica Festival 2017 and 2020, CHI 2018, Mutek Montreal 2018, and Contemporary Istanbul PlugIn 2019. He is an Assistant Professor in Interactive AI at Chalmers University of Technology in Gothenburg, Sweden.
Showing 20 publications
Sound Design Strategies for Latent Audio Space Explorations using Deep Learning Architectures
Caring Trouble and Musical AI: Considerations towards a Feminist Musical AI
Bottom-up live coding: Analysis of continuous interactions towards predicting programming behaviours
The Neuralacoustics Project: Exploring Deep-Learnin for Lightweight Numerical Modeling Synthesis
instance: Soma-based multi-user interaction design for the telematic sonic arts
Latent Timbre Synthesis: Audio-based Variational Auto-Encoders for Music Composition Applications
Chatterbox: an interactive system of gibberish agents
Respire: Virtual Reality Art with Musical Agent Guided by Respiratory Interaction
Initial Remarks on Analyzing Acousmatic Music from the Perspective of Multi-agents
Audio-based Musical Artificial Intelligence and Audio-Reactive Visual Agents in Revive
Musical agents: A typology and state of the art towards Musical Metacreation
Respire: a Breath Away from the Experience in Virtual Environment
REVIVE: An Audio-Visual Performance with Musical and Visual Artificial Intelligence Agents
Quantitative Analysis of the Im pact of Mixing on Perceived Emotion of Soundscape Recordings
Ranking Based Experimental Music Emotion Recognition
Automatic Synthesizer Preset Generation with PresetGen
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