Publications

PhD THESIS

EPFL, Laboratory of Computational Neuroscience

Learning music composition with recurrent neural networks

16th Sound and Music Computing Conference

Colombo, F., & Gerstner, W. (2019). Learning to Generated Music with BachProp. In Sound and Music Computing Conference.

Annual Conference on Cognitive Computational Neuroscience (CCN)
September 6-8, 2017

Colombo, F., & Gerstner, W. (2017). BachProp: A Trainable Generative Model of Music Scores. In Conference on Cognitive Computational Neuroscience.

6th International Conference on Computational Intelligence in Music, Sound, Art and Design.

Colombo, F., Seeholzer, A., & Gerstner, W. (2017, April). Deep Artificial Composer: A Creative Neural Network Model for Automated Melody Generation. In International Conference on Evolutionary and Biologically Inspired Music and Art (pp. 81-96). Springer, Cham.

*Best paper award candidate

1st Conference on Computer Simulation of Musical Creativity

Colombo, F., Muscinelli, S. P., Seeholzer, A., Brea, J., & Gerstner, W. (2016). Algorithmic composition of melodies with deep recurrent neural networks. arXiv preprint arXiv:1606.07251.

Ecole Polytechnique Federale de Lausanne (EPFL)

Colombo, F. F. (2015). Music Learning with Long Short Term Memory Networks (No. EPFL-STUDENT-218541).