5 citations · 15 across the 20 of their papers we have counts for
23 papers
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…
Where Flow Matching Leaks: Characterising Membership Signals Along the Interpolation Path
Thomas Sesmat, Gabriel Meseguer-Brocal, Geoffroy Peeters
Understanding memorization in generative models remains challenging, with implications for copyright and privacy. Beyond verbatim reproduction, models can encode subtler traces of…
S-SONDO: Self-Supervised Knowledge Distillation for General Audio Foundation Models
Mohammed Ali El Adlouni, Aurian Quelennec, Pierre Chouteau +2
General audio foundation models have recently achieved remarkable progress, enabling strong performance across diverse tasks. However, state-of-the-art models remain extremely larg…
S-PRESSO: Ultra Low Bitrate Sound Effect Compression With Diffusion Autoencoders And Offline Quantization
Zineb Lahrichi, Gaëtan Hadjeres, Gaël Richard +1
Neural audio compression models have recently achieved extreme compression rates, enabling efficient latent generative modeling. Conversely, latent generative models have been appl…
Twenty-Five Years of MIR Research: Achievements, Practices, Evaluations, and Future Challenges
Geoffroy Peeters, Zafar Rafii, Magdalena Fuentes +4
In this paper, we trace the evolution of Music Information Retrieval (MIR) over the past 25 years. While MIR gathers all kinds of research related to music informatics, a large par…
Controlling Contrastive Self-Supervised Learning with Knowledge-Driven Multiple Hypothesis: Application to Beat Tracking
Antonin Gagnere, Slim Essid, Geoffroy Peeters
Ambiguities in data and problem constraints can lead to diverse, equally plausible outcomes for a machine learning task. In beat and downbeat tracking, for instance, different list…
MATPAC++: Enhanced Masked Latent Prediction for Self-Supervised Audio Representation Learning
Aurian Quelennec, Pierre Chouteau, Geoffroy Peeters +1
Masked latent prediction has emerged as a leading paradigm in self-supervised learning (SSL), especially for general audio and music representation learning. While recent methods h…
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…
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…
Masked Latent Prediction and Classification for Self-Supervised Audio Representation Learning
Aurian Quelennec, Pierre Chouteau, Geoffroy Peeters +1
Recently, self-supervised learning methods based on masked latent prediction have proven to encode input data into powerful representations. However, during training, the learned l…
A Contrastive Self-Supervised Learning scheme for beat tracking amenable to few-shot learning
Antonin Gagnere, Geoffroy Peeters, Slim Essid
In this paper, we propose a novel Self-Supervised-Learning scheme to train rhythm analysis systems and instantiate it for few-shot beat tracking. Taking inspiration from the Contra…
Zero-shot Musical Stem Retrieval with Joint-Embedding Predictive Architectures
Alain Riou, Antonin Gagneré, Gaëtan Hadjeres +2
In this paper, we tackle the task of musical stem retrieval. Given a musical mix, it consists in retrieving a stem that would fit with it, i.e., that would sound pleasant if played…
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…
Stem-JEPA: A Joint-Embedding Predictive Architecture for Musical Stem Compatibility Estimation
Alain Riou, Stefan Lattner, Gaëtan Hadjeres +2
This paper explores the automated process of determining stem compatibility by identifying audio recordings of single instruments that blend well with a given musical context. To t…
Investigating Design Choices in Joint-Embedding Predictive Architectures for General Audio Representation Learning
Alain Riou, Stefan Lattner, Gaëtan Hadjeres +1
This paper addresses the problem of self-supervised general-purpose audio representation learning. We explore the use of Joint-Embedding Predictive Architectures (JEPA) for this ta…
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…
On the choice of the optimal temporal support for audio classification with Pre-trained embeddings
Aurian Quelennec, Michel Olvera, Geoffroy Peeters +1
Current state-of-the-art audio analysis systems rely on pre-trained embedding models, often used off-the-shelf as (frozen) feature extractors. Choosing the best one for a set of ta…
Blind estimation of audio effects using an auto-encoder approach and differentiable digital signal processing
Côme Peladeau, Geoffroy Peeters
Blind Estimation of Audio Effects (BE-AFX) aims at estimating the Audio Effects (AFXs) applied to an original, unprocessed audio sample solely based on the processed audio sample.…
Self-Similarity-Based and Novelty-based loss for music structure analysis
Geoffroy Peeters
Music Structure Analysis (MSA) is the task aiming at identifying musical segments that compose a music track and possibly label them based on their similarity. In this paper we pro…
PESTO: Pitch Estimation with Self-supervised Transposition-equivariant Objective
Alain Riou, Stefan Lattner, Gaëtan Hadjeres +1
In this paper, we address the problem of pitch estimation using Self Supervised Learning (SSL). The SSL paradigm we use is equivariance to pitch transposition, which enables our mo…