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Paul Magron

7 accepted papers

2021

Leveraging the Structure of Musical Preference in Content-Aware Music Recommendation

ICASSP 2021accepted

State-of-the-art music recommendation systems are based on collaborative filtering, which predicts a user’s interest from his listening habits and similarities with other users’ profiles. These approaches are agnostic to the song content, and therefore face the cold-start problem: they cannot recomm…

Cited by 0SourceScholar
2021

Phase Recovery with Bregman Divergences for Audio Source Separation

ICASSP 2021accepted

Time-frequency audio source separation is usually achieved by estimating the short-time Fourier transform (STFT) magnitude of each source, and then applying a phase recovery algorithm to retrieve time-domain signals. In particular, the multiple input spectrogram inversion (MISI) algorithm has shown…

Cited by 1SourceScholar
2016

Common fate model for unison source separation

ICASSP 2016accepted

In this paper we present a novel source separation method aiming to overcome the difficulty of modelling non-stationary signals. The method can be applied to mixtures of musical instruments with frequency and/or amplitude modulation, e.g. typically caused by vibrato. It is based on a signal represen…

Cited by 30SourceScholar
2016

Complex NMF under phase constraints based on signal modeling: Application to audio source separation

ICASSP 2016accepted

Nonnegative Matrix Factorization (NMF) is a powerful tool for decomposing mixtures of audio signals in the Time-Frequency (TF) domain. In the source separation framework, the phase recovery for each extracted component is necessary for synthesizing time-domain signals. The Complex NMF (CNMF) model a…

Cited by 0SourceScholar
2015

Phase recovery in NMF for audio source separation: An insightful benchmark

ICASSP 2015accepted

Nonnegative Matrix Factorization (NMF) is a powerful tool for decomposing mixtures of audio signals in the Time-Frequency (TF) domain. In applications such as source separation, the phase recovery for each extracted component is a major issue since it often leads to audible artifacts. In this paper,…

Cited by 0SourceScholar