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Thomas Oberlin

7 accepted papers

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
2020

Ordinal Non-negative Matrix Factorization for Recommendation

ICML 2020poster

We introduce a new non-negative matrix factorization (NMF) method for ordinal data, called OrdNMF. Ordinal data are categorical data which exhibit a natural ordering between the categories. In particular, they can be found in recommender systems, either with explicit data (such as ratings) or implic…

2020

Unsupervised Change Detection for Multimodal Remote Sensing Images via Coupled Dictionary Learning and Sparse Coding

ICASSP 2020accepted

Archetypal scenarios for change detection generally consider two images acquired through sensors of the same modality. The resolution dissimilarity is often bypassed though a simple preprocessing, applied independently on each image to bring them to the same resolution. However, in some important si…

Cited by 0SourceScholar
2019

Recommendation from Raw Data with Adaptive Compound Poisson Factorization

UAI 2019poster

Count data are often used in recommender systems: they are widespread (song play counts, product purchases, clicks on web pages) and can reveal user preference without any explicit rating from the user. Such data are known to be sparse, over-dispersed and bursty, which makes their direct use in reco…

2019

Unmixing Dynamic Pet Images: Combining Spatial Heterogeneity and Non-gaussian Noise

ICASSP 2019accepted

An important task when processing dynamic PET images is to identify the time-activity curves (TACs) of the pure tissues, along with their corresponding spatial proportions. This step, often referred to as unmixing or factor analysis, is based on a loss function which measures the discrepancy between…

Cited by 0SourceScholar