| Imposing Higher-Level Structure in Polyphonic Music Generation using Convolutional Restricted Boltzmann Machines and Constraints S Lattner, M Grachten, G Widmer Journal of Creative Music Systems 2 (2), 2018 | 55 | 2018 |
| Audio-to-Score Alignment using Transposition-Invariant Features A Arzt, S Lattner 19th International Society for Music Information Retrieval Conference (ISMIR …, 2018 | 12 | 2018 |
| Learning Transformations of Musical Material using Gated Autoencoders S Lattner, M Grachten, G Widmer Proceedings of the 2nd conference on computer simulation of musical …, 2017 | 9* | 2017 |
| Probabilistic Segmentation of Musical Sequences Using Restricted Boltzmann Machines S Lattner, M Grachten, K Agres, CEC Chacón International Conference on Mathematics and Computation in Music, 323-334, 2015 | 9 | 2015 |
| A Predictive Model for Music Based on Learned Interval Representations S Lattner, M Grachten, G Widmer 19th International Society for Music Information Retrieval Conference (ISMIR …, 2018 | 8 | 2018 |
| Learning Transposition-Invariant Interval Features from Symbolic Music and Audio S Lattner, M Grachten, G Widmer 19th International Society for Music Information Retrieval Conference (ISMIR …, 2018 | 8 | 2018 |
| Developing Tonal Perception through Unsupervised Learning CEC Chacón, S Lattner, M Grachten ISMIR 2014, 195-200, 2014 | 7 | 2014 |
| DrumNet: High-Level Control of Drum Track Generation Using Learned Patterns of Rhythmic Interaction S Lattner, M Grachten IEEE Workshop on Applications of Signal Processing to Audio and Acoustics …, 2019 | 6* | 2019 |
| Hierarchical temporal memory-investigations, ideas, and experiments S Lattner Master’s thesis, Johannes Kepler Universität, 2014 | 6 | 2014 |
| Harmonics co-occurrences bootstrap pitch and tonality perception in music: Evidence from a statistical unsupervised learning model K Agres, C Cancino, M Grachten, S Lattner Proceedings of the Cognitive Science Society, 2015 | 4 | 2015 |
| DrumGAN: Synthesis of drum sounds with timbral feature conditioning using Generative Adversarial Networks J Nistal, S Lattner, G Richard 19th International Society for Music Information Retrieval Conference (ISMIR), 2020 | 3 | 2020 |
| Learning Complex Basis Functions for Invariant Representations of Audio S Lattner, M Dörfler, A Arzt 20th International Society for Music Information Retrieval Conference (ISMIR …, 2019 | 3 | 2019 |
| Pseudo-supervised training improves unsupervised melody segmentation S Lattner, CEC Chacón, M Grachten Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015 | 3 | 2015 |
| Comparing representations for audio synthesis using generative adversarial networks J Nistal, S Lattner, G Richard 27th European Signal Processing Conference (EUSIPCO) 2019, 2020 | 1 | 2020 |
| BassNet: A Variational Gated Autoencoder for Conditional Generation of Bass Guitar Tracks with Learned Interactive Control M Grachten, S Lattner, E Deruty Applied Sciences 10 (18), 6627, 2020 | 1 | 2020 |
| Improving content-invariance in gated autoencoders for 2d and 3d object rotation S Lattner, M Grachten arXiv preprint arXiv:1707.01357, 2017 | 1 | 2017 |
| Modeling Musical Structure with Artificial Neural Networks S Lattner PhD thesis, Johannes Kepler University, Linz, 2019 | | 2019 |
| A Computational Approach to Modelling the Perception of Pitch and Tonality in Music. K Agres, C Cancino, M Grachten, S Lattner CogSci, 2015 | | 2015 |
| Binaurale Quellen-Lokalisierung mit Wahrscheinlichkeits-Agenten-Tracking S Lattner | | |
| TOWARDS AN INVERTIBLE RHYTHM REPRESENTATION G Carayannis | | |
Maarten GrachtenIndependent Machine Learning ConsultantBestätigte E-Mail-Adresse bei ofai.at
