We are always happy to have PhD interns on the team. If you are a PhD student interested in our activities, feel free to contact us!

Publications.

2019

Variation Network: Learning High-level Attributes for Controlled Input Manipulation

Gaëtan Hadjeres, Frank Nielsen

VAE, latent space disentanglement, adversarial learning

2019

On power chi expansions of f-divergences

Frank Nielsen, Gaëtan Hadjeres

f-divergences, chi-squared distance, exponential family, Taylor expansions, binomial and multinomial theorems, analytic formula, bounded density ratio

ISMIR 2018

A Predictive Model for Music Based on Learned Interval Representations

Stefan Lattner, Maarten Grachten, Gerhard Widmer

recurrent gated autoencoder, relative pitch modelling, interval representation

ISMIR 2018

Learning Transposition-Invariant Interval Features from Symbolic Music and Audio

Stefan Lattner, Maarten Grachten, Gerhard Widmer

repeated sections discovery, interval representation, transposition invariance

ISMIR 2018

Audio-to-Score Alignment using Transposition-invariant Features

Andreas Arzt, Stefan Lattner

audio-to-score alignment, gated autoencoder, local pitch intervals, transposition invariance

Neural Computing and Applications 2018

Anticipation-RNN: Enforcing Unary Constraints in Sequence Generation, with Application to Interactive Music Generation

Gaëtan Hadjeres, Frank Nielsen

automatic symbolic music generation, recurrent neural networks, interactive models, unary constraints

SSCI 2017

GLSR-VAE: Geodesic Latent Space Regularization for Variational Autoencoder Architectures

Gaëtan Hadjeres, François Pachet, Frank Nielsen

VAE, latent space regularization, disentaglement

ICML 2017

DeepBach: a Steerable Model for Bach Chorales Generation

Gaëtan Hadjeres, François Pachet, Frank Nielsen

musical inpainting, Bach chorales