125 citations · 245 across the 22 of their papers we have counts for
12 papers · 1 filter
Woosh: A Sound Effects Foundation Model
Gaëtan Hadjeres, Marc Ferras, Khaled Koutini +7
The audio research community depends on open generative models as foundational tools for building novel approaches and establishing baselines. In this report, we present Woosh, Son…
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…
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…
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…
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…
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…
The Piano Inpainting Application
Gaëtan Hadjeres, Léopold Crestel
Autoregressive models are now capable of generating high-quality minute-long expressive MIDI piano performances. Even though this progress suggests new tools to assist music compos…
Spectrogram Inpainting for Interactive Generation of Instrument Sounds
Théis Bazin, Gaëtan Hadjeres, Philippe Esling +1
Modern approaches to sound synthesis using deep neural networks are hard to control, especially when fine-grained conditioning information is not available, hindering their adoptio…
Incorporating Music Knowledge in Continual Dataset Augmentation for Music Generation
Alisa Liu, Alexander Fang, Gaëtan Hadjeres +2
Deep learning has rapidly become the state-of-the-art approach for music generation. However, training a deep model typically requires a large training set, which is often not avai…
Neural Drum Machine : An Interactive System for Real-time Synthesis of Drum Sounds
Cyran Aouameur, Philippe Esling, Gaëtan Hadjeres
In this work, we introduce a system for real-time generation of drum sounds. This system is composed of two parts: a generative model for drum sounds together with a Max4Live plugi…
Deep Learning Techniques for Music Generation -- A Survey
Jean-Pierre Briot, Gaëtan Hadjeres, François-David Pachet
This paper is a survey and an analysis of different ways of using deep learning (deep artificial neural networks) to generate musical content. We propose a methodology based on fiv…