24 citations · 102 across the 17 of their papers we have counts for
23 papers
A Phoneme-Scale Assessment of Multichannel Speech Enhancement Algorithms
Nasser-Eddine Monir, Paul Magron, Romain Serizel
In the intricate acoustic landscapes where speech intelligibility is challenged by noise and reverberation, multichannel speech enhancement emerges as a promising solution for indi…
Spectrogram Inversion for Audio Source Separation via Consistency, Mixing, and Magnitude Constraints
Paul Magron, Tuomas Virtanen
Audio source separation is often achieved by estimating the magnitude spectrogram of each source, and then applying a phase recovery (or spectrogram inversion) algorithm to retriev…
Signal Inpainting from Fourier Magnitudes
Louis Bahrman, Marina Krémé, Paul Magron +1
Signal inpainting is the task of restoring degraded or missing samples in a signal. In this paper we address signal inpainting when Fourier magnitudes are observed. We propose a ma…
Algorithms for audio inpainting based on probabilistic nonnegative matrix factorization
Ondřej Mokrý, Paul Magron, Thomas Oberlin +1
Audio inpainting, i.e., the task of restoring missing or occluded audio signal samples, usually relies on sparse representations or autoregressive modeling. In this paper, we propo…
A majorization-minimization algorithm for nonnegative binary matrix factorization
Paul Magron, Cédric Févotte
This paper tackles the problem of decomposing binary data using matrix factorization. We consider the family of mean-parametrized Bernoulli models, a class of generative models tha…
Learning the Proximity Operator in Unfolded ADMM for Phase Retrieval
Pierre-Hugo Vial, Paul Magron, Thomas Oberlin +1
This paper considers the phase retrieval (PR) problem, which aims to reconstruct a signal from phaseless measurements such as magnitude or power spectrograms. PR is generally handl…
A Sparsity-promoting Dictionary Model for Variational Autoencoders
Mostafa Sadeghi, Paul Magron
Structuring the latent space in probabilistic deep generative models, e.g., variational autoencoders (VAEs), is important to yield more expressive models and interpretable represen…
Neural content-aware collaborative filtering for cold-start music recommendation
Paul Magron, Cédric Févotte
State-of-the-art music recommender systems are based on collaborative filtering, which builds upon learning similarities between users and songs from the available listening data.…
Phase retrieval with Bregman divergences: Application to audio signal recovery
Pierre-Hugo Vial, Paul Magron, Thomas Oberlin +1
Phase retrieval aims to recover a signal from magnitude or power spectra measurements. It is often addressed by considering a minimization problem involving a quadratic cost functi…
Leveraging the structure of musical preference in content-aware music recommendation
Paul Magron, Cédric Févotte
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' pr…
Phase recovery with Bregman divergences for audio source separation
Paul Magron, Pierre-Hugo Vial, Thomas Oberlin +1
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 algori…
Phase retrieval with Bregman divergences and application to audio signal recovery
Pierre-Hugo Vial, Paul Magron, Thomas Oberlin +1
Phase retrieval (PR) aims to recover a signal from the magnitudes of a set of inner products. This problem arises in many audio signal processing applications which operate on a sh…
Online Spectrogram Inversion for Low-Latency Audio Source Separation
Paul Magron, Tuomas Virtanen
Audio source separation is usually achieved by estimating the short-time Fourier transform (STFT) magnitude of each source, and then applying a spectrogram inversion algorithm to r…
Language Modelling for Sound Event Detection with Teacher Forcing and Scheduled Sampling
Konstantinos Drossos, Shayan Gharib, Paul Magron +1
A sound event detection (SED) method typically takes as an input a sequence of audio frames and predicts the activities of sound events in each frame. In real-life recordings, the…
Unsupervised Adversarial Domain Adaptation Based On The Wasserstein Distance For Acoustic Scene Classification
Konstantinos Drossos, Paul Magron, Tuomas Virtanen
A challenging problem in deep learning-based machine listening field is the degradation of the performance when using data from unseen conditions. In this paper we focus on the aco…
Harmonic-Percussive Source Separation with Deep Neural Networks and Phase Recovery
Konstantinos Drossos, Paul Magron, Stylianos Ioannis Mimilakis +1
Harmonic/percussive source separation (HPSS) consists in separating the pitched instruments from the percussive parts in a music mixture. In this paper, we propose to apply the rec…
Complex ISNMF: a Phase-Aware Model for Monaural Audio Source Separation
Paul Magron, Tuomas Virtanen
This paper introduces a phase-aware probabilistic model for audio source separation. Classical source models in the short-term Fourier transform domain use circularly-symmetric Gau…
Model-based STFT phase recovery for audio source separation
Paul Magron, Roland Badeau, Bertrand David
For audio source separation applications, it is common to estimate the magnitude of the short-time Fourier transform (STFT) of each source. In order to further synthesizing time-do…
Lévy NMF for robust nonnegative source separation
Paul Magron, Roland Badeau, Antoine Liutkus
Source separation, which consists in decomposing data into meaningful structured components, is an active research topic in many areas, such as music and image signal processing, a…
Phase recovery in NMF for audio source separation: an insightful benchmark
Paul Magron, Roland Badeau, Bertrand David
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, th…
Phase reconstruction of spectrograms based on a model of repeated audio events
Paul Magron, Roland Badeau, Bertrand David
Phase recovery of modified spectrograms is a major issue in audio signal processing applications, such as source separation. This paper introduces a novel technique for estimating…
Phase reconstruction of spectrograms with linear unwrapping: application to audio signal restoration
Paul Magron, Roland Badeau, Bertrand David
This paper introduces a novel technique for reconstructing the phase of modified spectrograms of audio signals. From the analysis of mixtures of sinusoids we obtain relationships b…
Complex NMF under phase constraints based on signal modeling: application to audio source separation
Paul Magron, Roland Badeau, Bertrand David
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…