most citedVideo-to-Music Recommendation using Temporal Alignment of Segments

21 citations · 21 across the 7 of their papers we have counts for

collaborators

7 papers

cs.SD2024

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…

eess.SP2024

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…

cs.SD2024

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…

stat.ML2023

Resilient Multiple Choice Learning: A learned scoring scheme with application to audio scene analysis

Victor Letzelter, Mathieu Fontaine, Mickaël Chen +3

We introduce Resilient Multiple Choice Learning (rMCL), an extension of the MCL approach for conditional distribution estimation in regression settings where multiple targets may b…

eess.AS2023

Transfer Learning and Bias Correction with Pre-trained Audio Embeddings

Changhong Wang, Gaël Richard, Brian McFee

Deep neural network models have become the dominant approach to a large variety of tasks within music information retrieval (MIR). These models generally require large amounts of (…

cs.MM202321 cited

Video-to-Music Recommendation using Temporal Alignment of Segments

Laure Prétet, Gaël Richard, Clément Souchier +1

We study cross-modal recommendation of music tracks to be used as soundtracks for videos. This problem is known as the music supervision task. We build on a self-supervised system…