Stefan Lattner serves as a research leader on the music team, where he focuses on generative AI for music production, music information retrieval, and representation learning. He earned his PhD in 2019 from Johannes Kepler University (JKU) in Linz, Austria, following his research at the Austrian Research Institute for Artificial Intelligence in Vienna and the Institute of Computational Perception in Linz. His studies centered on the modeling of musical structure, encompassing transformation learning and computational relative pitch perception. Stefan’s current interests include human-computer interaction in music creation, live staging, and information theory in music. He specializes in latent diffusion, self-supervised learning, generative sequence models, computational short-term memories, and models of human perception.

 Awards

Best Paper Award ISMIR Conference (2019):

S. Lattner, M. Dörfler, A. Arzt; Learning Complex Basis Functions for Invariant Representations of Audio.

Best Paper Award ISMIR Conference (2023):A. Riou, S. Lattner, G. Hadjeres, G. Peeters; Pesto: Pitch estimation with self-supervised transposition-equivariant objective.