Clement Laroche — accepted AI-conference papers

2020

Demystifying Orthogonal Monte Carlo and Beyond

Han Lin, Haoxian Chen, Krzysztof M Choromanski, Tianyi Zhang, Clement Laroche

NeurIPS 2020poster

Orthogonal Monte Carlo (OMC) is a very effective sampling algorithm imposing structural geometric conditions (orthogonality) on samples for variance reduction. Due to its simplicity and superior performance as compared to its Quasi Monte Carlo counterparts, OMC is used in a wide spectrum of challeng…

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2017

Drum extraction in single channel audio signals using multi-layer Non negative Matrix Factor Deconvolution

Clement Laroche, Hélène Papadopoulos, Matthieu Kowalski, Gaël Richard

ICASSP 2017accepted

In this paper, we propose a supervised multilayer factorization method designed for harmonic/percussive source separation and drum extraction. Our method decomposes the audio signals in sparse orthogonal components which capture the harmonic content, while the drum is represented by an extension of…

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