4 citations · 7 across the 5 of their papers we have counts for
6 papers
Comparing Representations for Audio Synthesis Using Generative Adversarial Networks
Javier Nistal, Stefan Lattner, Gaël Richard
In this paper, we compare different audio signal representations, including the raw audio waveform and a variety of time-frequency representations, for the task of audio synthesis…
Modeling Musical Structure with Artificial Neural Networks
Stefan Lattner
In recent years, artificial neural networks (ANNs) have become a universal tool for tackling real-world problems. ANNs have also shown great success in music-related tasks includin…
High-Level Control of Drum Track Generation Using Learned Patterns of Rhythmic Interaction
Stefan Lattner, Maarten Grachten
Spurred by the potential of deep learning, computational music generation has gained renewed academic interest. A crucial issue in music generation is that of user control, especia…
Learning Complex Basis Functions for Invariant Representations of Audio
Stefan Lattner, Monika Dörfler, Andreas Arzt
Learning features from data has shown to be more successful than using hand-crafted features for many machine learning tasks. In music information retrieval (MIR), features learned…
Learning Musical Relations using Gated Autoencoders
Stefan Lattner, Maarten Grachten, Gerhard Widmer
Music is usually highly structured and it is still an open question how to design models which can successfully learn to recognize and represent musical structure. A fundamental pr…
Improving Content-Invariance in Gated Autoencoders for 2D and 3D Object Rotation
Stefan Lattner, Maarten Grachten
Content-invariance in mapping codes learned by GAEs is a useful feature for various relation learning tasks. In this paper we show that the content-invariance of mapping codes for…