14 citations · 64 across the 21 of their papers we have counts for
9 papers · 1 filter
Episodic fine-tuning prototypical networks for optimization-based few-shot learning: Application to audio classification
Xuanyu Zhuang, Geoffroy Peeters, Gaël Richard
The Prototypical Network (ProtoNet) has emerged as a popular choice in Few-shot Learning (FSL) scenarios due to its remarkable performance and straightforward implementation. Build…
WaveTransfer: A Flexible End-to-end Multi-instrument Timbre Transfer with Diffusion
Teysir Baoueb, Xiaoyu Bie, Hicham Janati +1
As diffusion-based deep generative models gain prevalence, researchers are actively investigating their potential applications across various domains, including music synthesis and…
Model-Based Deep Learning for Music Information Research
Gael Richard, Vincent Lostanlen, Yi-Hsuan Yang +1
In this article, we investigate the notion of model-based deep learning in the realm of music information research (MIR). Loosely speaking, we refer to the term model-based deep le…
Structure-informed Positional Encoding for Music Generation
Manvi Agarwal, Changhong Wang, Gaël Richard
Music generated by deep learning methods often suffers from a lack of coherence and long-term organization. Yet, multi-scale hierarchical structure is a distinctive feature of musi…
GLA-Grad: A Griffin-Lim Extended Waveform Generation Diffusion Model
Haocheng Liu, Teysir Baoueb, Mathieu Fontaine +2
Diffusion models are receiving a growing interest for a variety of signal generation tasks such as speech or music synthesis. WaveGrad, for example, is a successful diffusion model…
SpecDiff-GAN: A Spectrally-Shaped Noise Diffusion GAN for Speech and Music Synthesis
Teysir Baoueb, Haocheng Liu, Mathieu Fontaine +2
Generative adversarial network (GAN) models can synthesize highquality audio signals while ensuring fast sample generation. However, they are difficult to train and are prone to se…
A fully differentiable model for unsupervised singing voice separation
Gael Richard, Pierre Chouteau, Bernardo Torres
A novel model was recently proposed by Schulze-Forster et al. in [1] for unsupervised music source separation. This model allows to tackle some of the major shortcomings of existin…
Unsupervised Harmonic Parameter Estimation Using Differentiable DSP and Spectral Optimal Transport
Bernardo Torres, Geoffroy Peeters, Gaël Richard
In neural audio signal processing, pitch conditioning has been used to enhance the performance of synthesizers. However, jointly training pitch estimators and synthesizers is a cha…
Singer Identity Representation Learning using Self-Supervised Techniques
Bernardo Torres, Stefan Lattner, Gaël Richard
Significant strides have been made in creating voice identity representations using speech data. However, the same level of progress has not been achieved for singing voices. To br…