125 citations · 245 across the 22 of their papers we have counts for
24 papers
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…
Vector Quantized Contrastive Predictive Coding for Template-based Music Generation
Gaëtan Hadjeres, Léopold Crestel
In this work, we propose a flexible method for generating variations of discrete sequences in which tokens can be grouped into basic units, like sentences in a text or bars in musi…
A note on the quasiconvex Jensen divergences and the quasiconvex Bregman divergences derived thereof
Frank Nielsen, Gaëtan Hadjeres
We first introduce the class of strictly quasiconvex and strictly quasiconcave Jensen divergences which are oriented (asymmetric) distances, and study some of their properties. We…
NONOTO: A Model-agnostic Web Interface for Interactive Music Composition by Inpainting
Théis Bazin, Gaëtan Hadjeres
Inpainting-based generative modeling allows for stimulating human-machine interactions by letting users perform stylistically coherent local editions to an object using a statistic…
Learning to Traverse Latent Spaces for Musical Score Inpainting
Ashis Pati, Alexander Lerch, Gaëtan Hadjeres
Music Inpainting is the task of filling in missing or lost information in a piece of music. We investigate this task from an interactive music creation perspective. To this end, a…
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…
On power chi expansions of -divergences
Frank Nielsen, Gaëtan Hadjeres
We consider both finite and infinite power chi expansions of -divergences derived from Taylor's expansions of smooth generators, and elaborate on cases where these expansions yi…
Variation Network: Learning High-level Attributes for Controlled Input Manipulation
Gaëtan Hadjeres, Frank Nielsen
This paper presents the Variation Network (VarNet), a generative model providing means to manipulate the high-level attributes of a given input. The originality of our approach is…
Monte Carlo Information Geometry: The dually flat case
Frank Nielsen, Gaëtan Hadjeres
Exponential families and mixture families are parametric probability models that can be geometrically studied as smooth statistical manifolds with respect to any statistical diverg…
Interactive Music Generation with Positional Constraints using Anticipation-RNNs
Gaëtan Hadjeres, Frank Nielsen
Recurrent Neural Networks (RNNS) are now widely used on sequence generation tasks due to their ability to learn long-range dependencies and to generate sequences of arbitrary lengt…
Deep rank-based transposition-invariant distances on musical sequences
Gaëtan Hadjeres, Frank Nielsen
Distances on symbolic musical sequences are needed for a variety of applications, from music retrieval to automatic music generation. These musical sequences belong to a given corp…
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…
GLSR-VAE: Geodesic Latent Space Regularization for Variational AutoEncoder Architectures
Gaëtan Hadjeres, Frank Nielsen, François Pachet
VAEs (Variational AutoEncoders) have proved to be powerful in the context of density modeling and have been used in a variety of contexts for creative purposes. In many settings, t…
DeepBach: a Steerable Model for Bach Chorales Generation
Gaëtan Hadjeres, François Pachet, Frank Nielsen
This paper introduces DeepBach, a graphical model aimed at modeling polyphonic music and specifically hymn-like pieces. We claim that, after being trained on the chorale harmonizat…
Style Imitation and Chord Invention in Polyphonic Music with Exponential Families
Gaëtan Hadjeres, Jason Sakellariou, François Pachet
Modeling polyphonic music is a particularly challenging task because of the intricate interplay between melody and harmony. A good model should satisfy three requirements: statisti…