Publications: Mr Daniel Stoller

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Vatolkin I, Stoller D (2019). Evolutionary multi-objective training set selection of data instances and augmentations for vocal detection. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 11453 LNCS, 201-216.

10.1007/978-3-030-16667-0_14

Stoller D, Akkermans V, Dixon S (2018). Detection of cut-points for automatic music rearrangement. IEEE International Workshop on Machine Learning for Signal Processing, MLSP. Conference: IEEE International Workshop on Machine Learning for Signal Processing, MLSP vol. 2018-September,

10.1109/MLSP.2018.8516706

Stoller D, Ewert S, Dixon S (2018). Adversarial Semi-Supervised Audio Source Separation Applied to Singing Voice Extraction. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. Conference: IEEE International Conference on Acoustics, Speech and Signal Processing vol. 2018-April, 2391-2395.

10.1109/ICASSP.2018.8461722

Stoller D, Vatolkin I, Müller H(2018). Intuitive and efficient computer-aided music rearrangement with optimised processing of audio transitions. Journal of New Music Research1-22.

10.1080/09298215.2018.1473448

STOLLER D, Ewert S, DIXON S (2018). Jointly detecting and separating singing voice: a multi-task approach. Conference: 14th International Conference on Latent Variable Analysis and Signal Separation

10.1007/978-3-319-93764-9_31

Stoller D, Mauch M, Vatolkin I, Weihs C(2015). Impact of Frame Size and Instrumentation on Chroma-based Automatic Chord Recognition. Data Science, Learning by Latent Structures, and Knowledge Discovery, Editors: Lausen, B, Krolak-Schwerdt, S, Bohmer, M, vol. 2015, Springer

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