activity
20152022
most citedExploring multimodal data fusion through joint decompositions with flexible couplings

62 citations · 105 across the 12 of their papers we have counts for

collaborators

12 papers

cs.SD2022

Polytopic Analysis of Music

Axel Marmoret, Jérémy E. Cohen, Frédéric Bimbot

Structural segmentation of music refers to the task of finding a symbolic representation of the organisation of a song, reducing the musical flow to a partition of non-overlapping…

cs.SD2022

Convolutive Block-Matching Segmentation Algorithm with Application to Music Structure Analysis

Axel Marmoret, Jérémy E. Cohen, Frédéric Bimbot

Music Structure Analysis (MSA) consists of representing a song in sections (such as ``chorus'', ``verse'', ``solo'' etc), and can be seen as the retrieval of a simplified organizat…

cs.SD2022★ 1 cited

Semi-Supervised Convolutive NMF for Automatic Piano Transcription

Haoran Wu, Axel Marmoret, Jérémy E. Cohen

Automatic Music Transcription, which consists in transforming an audio recording of a musical performance into symbolic format, remains a difficult Music Information Retrieval task…

cs.SD2022★ 3 cited

Barwise Compression Schemes for Audio-Based Music Structure Analysis

Axel Marmoret, Jérémy E. Cohen, Frédéric Bimbot

Music Structure Analysis (MSA) consists in segmenting a music piece in several distinct sections. We approach MSA within a compression framework, under the hypothesis that the stru…

cs.SD2021

Nonnegative Tucker Decomposition with Beta-divergence for Music Structure Analysis of Audio Signals

Axel Marmoret, Florian Voorwinden, Valentin Leplat +2

Nonnegative Tucker decomposition (NTD), a tensor decomposition model, has received increased interest in the recent years because of its ability to blindly extract meaningful patte…

cs.LG2021★ 2 cited

Pareto GAN: Extending the Representational Power of GANs to Heavy-Tailed Distributions

Todd Huster, Jeremy E. J. Cohen, Zinan Lin +5

Generative adversarial networks (GANs) are often billed as "universal distribution learners", but precisely what distributions they can represent and learn is still an open questio…