14 citations · 64 across the 21 of their papers we have counts for
29 papers
Multi-Rate Bandwidth Extension by Token Completion in Neural Audio Codecs
Benoît Ginies, Olivier Fercoq, Gaël Richard
Bandwidth extension, the task of reconstructing the high-frequency components of an audio signal from its low-passed counterpart, is a long-standing problem in audio processing. In…
SonicCaps: Large-Scale Diverse and Fine-Grained Captioning for Improved Audio-Retrieval
Zineb Lahrichi, Marc Ferras, Gaël Richard +1
Recent advances in audio-language modeling have been driven by large-scale audio captioning datasets. However, existing datasets remain limited by low semantic diversity, generic d…
Harmonic-Percussive Disentangled Neural Audio Codec for Bandwidth Extension
Benoît Giniès, Xiaoyu Bie, Olivier Fercoq +1
Bandwidth extension, the task of reconstructing the high-frequency components of an audio signal from its low-passed counterpart, is a long-standing problem in audio processing. In…
Is Phase Really Needed for Weakly-Supervised Dereverberation ?
Marius Rodrigues, Louis Bahrman, Roland Badeau +1
In unsupervised or weakly-supervised approaches for speech dereverberation, the target clean (dry) signals are considered to be unknown during training. In that context, evaluating…
Soft Disentanglement in Frequency Bands for Neural Audio Codecs
Benoit Ginies, Xiaoyu Bie, Olivier Fercoq +1
In neural-based audio feature extraction, ensuring that representations capture disentangled information is crucial for model interpretability. However, existing disentanglement me…
Translation-Equivariant Self-Supervised Learning for Pitch Estimation with Optimal Transport
Bernardo Torres, Alain Riou, Gaël Richard +1
In this paper, we propose an Optimal Transport objective for learning one-dimensional translation-equivariant systems and demonstrate its applicability to single pitch estimation.…
PESTO: Real-Time Pitch Estimation with Self-supervised Transposition-equivariant Objective
Alain Riou, Bernardo Torres, Ben Hayes +4
In this paper, we introduce PESTO, a self-supervised learning approach for single-pitch estimation using a Siamese architecture. Our model processes individual frames of a Variable…
Episode-specific Fine-tuning for Metric-based Few-shot Learners with Optimization-based Training
Xuanyu Zhuang, Geoffroy Peeters, Gaël Richard
In few-shot classification tasks (so-called episodes), a small set of labeled support samples is provided during inference to aid the classification of unlabeled query samples. Met…
Diff-TONE: Timestep Optimization for iNstrument Editing in Text-to-Music Diffusion Models
Teysir Baoueb, Xiaoyu Bie, Xi Wang +1
Breakthroughs in text-to-music generation models are transforming the creative landscape, equipping musicians with innovative tools for composition and experimentation like never b…
The Inverse Drum Machine: Source Separation Through Joint Transcription and Analysis-by-Synthesis
Bernardo Torres, Geoffroy Peeters, Gael Richard
We present the Inverse Drum Machine, a novel approach to Drum Source Separation that leverages an analysis-by-synthesis framework combined with deep learning. Unlike recent supervi…
QINCODEC: Neural Audio Compression with Implicit Neural Codebooks
Zineb Lahrichi, Gaëtan Hadjeres, Gael Richard +1
Neural audio codecs, neural networks which compress a waveform into discrete tokens, play a crucial role in the recent development of audio generative models. State-of-the-art code…
AnCoGen: Analysis, Control and Generation of Speech with a Masked Autoencoder
Samir Sadok, Simon Leglaive, Laurent Girin +2
This article introduces AnCoGen, a novel method that leverages a masked autoencoder to unify the analysis, control, and generation of speech signals within a single model. AnCoGen…
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…
Tackling Interpretability in Audio Classification Networks with Non-negative Matrix Factorization
Jayneel Parekh, Sanjeel Parekh, Pavlo Mozharovskyi +2
This paper tackles two major problem settings for interpretability of audio processing networks, post-hoc and by-design interpretation. For post-hoc interpretation, we aim to inter…
Rate-Distortion Theoretic Generalization Bounds for Stochastic Learning Algorithms
Milad Sefidgaran, Amin Gohari, Gaël Richard +1
Understanding generalization in modern machine learning settings has been one of the major challenges in statistical learning theory. In this context, recent years have witnessed t…
Listen to Interpret: Post-hoc Interpretability for Audio Networks with NMF
Jayneel Parekh, Sanjeel Parekh, Pavlo Mozharovskyi +2
This paper tackles post-hoc interpretability for audio processing networks. Our goal is to interpret decisions of a network in terms of high-level audio objects that are also liste…