Bio
Daniel Stoller is a Research Scientist in the Music Intelligence team at Spotify. He previously received his PhD involving machine learning for music information retrieval from Queen Mary University of London.
His research involves machine learning methods applicable across domains, including generative modelling using diffusion models and generative adversarial networks. He applies these methods to audio and music processing tasks, such as music generation, source separation, lyrics alignment, and multimodal music understanding.
Efficient Representation Learning for Music Via Likelihood Factorisation of a Variational Autoencoder
Ningzhi Wang, Daniel Stoller, Simon Dixon
IEEE International Workshop on Machine Learning for Signal Processing (MLSP) • 2025
LLark: A Multimodal Instruction-Following Language Model for Music
Joshua P. Gardner, Simon Durand, Daniel Stoller, Rachel M. Bittner
International Conference on Machine Learning (ICML) • 2024
Contrastive Learning-Based Audio to Lyrics Alignment for Multiple Languages
Simon Durand, Daniel Stoller, Sebastian Ewert
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) • 2023