activity
20172023
most citedEnergy Consumption of Deep Generative Audio Models

4 citations · 7 across the 5 of their papers we have counts for

collaborators

7 papers

cs.LG20232 cited

miditok: A Python package for MIDI file tokenization

Nathan Fradet, Jean-Pierre Briot, Fabien Chhel +2

Recent progress in natural language processing has been adapted to the symbolic music modality. Language models, such as Transformers, have been used with symbolic music for a vari…

cs.SD20231 cited

Impact of time and note duration tokenizations on deep learning symbolic music modeling

Nathan Fradet, Nicolas Gutowski, Fabien Chhel +1

Symbolic music is widely used in various deep learning tasks, including generation, transcription, synthesis, and Music Information Retrieval (MIR). It is mostly employed with disc…

cs.SD2022

An adaptive music generation architecture for games based on the deep learning Transformer mode

Gustavo Amaral Costa dos Santos, Augusto Baffa, Jean-Pierre Briot +2

This paper presents an architecture for generating music for video games based on the Transformer deep learning model. Our motivation is to be able to customize the generation acco…

cs.SD2021

Music Tempo Estimation via Neural Networks -- A Comparative Analysis

Mila Soares de Oliveira de Souza, Pedro Nuno de Souza Moura, Jean-Pierre Briot

This paper presents a comparative analysis on two artificial neural networks (with different architectures) for the task of tempo estimation. For this purpose, it also proposes the…

cs.LG20214 cited

Energy Consumption of Deep Generative Audio Models

Constance Douwes, Philippe Esling, Jean-Pierre Briot

In most scientific domains, the deep learning community has largely focused on the quality of deep generative models, resulting in highly accurate and successful solutions. However…

eess.AS2020

From Artificial Neural Networks to Deep Learning for Music Generation -- History, Concepts and Trends

Jean-Pierre Briot

The current wave of deep learning (the hyper-vitamined return of artificial neural networks) applies not only to traditional statistical machine learning tasks: prediction and clas…