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Multichannel audio source separation with deep neural networks

Aditya Arie Nugraha 1 Antoine Liutkus 1 Emmanuel Vincent 1
1 MULTISPEECH - Speech Modeling for Facilitating Oral-Based Communication
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : This technical report considers the problem of multichannel audio source separation. A few studies have addressed the problem of single-channel audio source separation with deep neural networks (DNNs). We introduce a new framework for multichannel source separation where (1) spectral and spatial parameters are updated iteratively similarly to the expectation-maximization (EM) algorithm and (2) DNNs are used in the spectral updates. We evaluated several systems based on the proposed framework by participating in the "professionally-produced music recording" task of SiSEC 2015. Experimental results show that the framework performed well in separating singing voice and other instruments from a mixture containing multiple musical instruments.
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Contributor : Aditya Arie Nugraha <>
Submitted on : Friday, June 12, 2015 - 4:32:13 PM
Last modification on : Saturday, November 16, 2019 - 7:04:01 PM
Document(s) archivé(s) le : Tuesday, April 25, 2017 - 7:38:28 AM


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  • HAL Id : hal-01163369, version 1


Aditya Arie Nugraha, Antoine Liutkus, Emmanuel Vincent. Multichannel audio source separation with deep neural networks. [Research Report] RR-8740, INRIA. 2015. ⟨hal-01163369v1⟩



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