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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…

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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…

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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…

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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…

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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,…

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