21 citations · 61 across the 8 of their papers we have counts for
15 papers
DarkGAN: Exploiting Knowledge Distillation for Comprehensible Audio Synthesis with GANs
Javier Nistal, Stefan Lattner, Gaël Richard
Generative Adversarial Networks (GANs) have achieved excellent audio synthesis quality in the last years. However, making them operable with semantically meaningful controls remain…
Probabilistic semi-nonnegative matrix factorization: a Skellam-based framework
Benoit Fuentes, Gaël Richard
We present a new probabilistic model to address semi-nonnegative matrix factorization (SNMF), called Skellam-SNMF. It is a hierarchical generative model consisting of prior compone…
VQCPC-GAN: Variable-Length Adversarial Audio Synthesis Using Vector-Quantized Contrastive Predictive Coding
Javier Nistal, Cyran Aouameur, Stefan Lattner +1
Influenced by the field of Computer Vision, Generative Adversarial Networks (GANs) are often adopted for the audio domain using fixed-size two-dimensional spectrogram representatio…
Cross-Modal Music-Video Recommendation: A Study of Design Choices
Laure Pretet, Gael Richard, Geoffroy Peeters
In this work, we study music/video cross-modal recommendation, i.e. recommending a music track for a video or vice versa. We rely on a self-supervised learning paradigm to learn fr…
Self-Supervised VQ-VAE for One-Shot Music Style Transfer
Ondřej Cífka, Alexey Ozerov, Umut Şimşekli +1
Neural style transfer, allowing to apply the artistic style of one image to another, has become one of the most widely showcased computer vision applications shortly after its intr…
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…