Deep Karaoke: Extracting Vocals from Musical Mixtures Using a Convolutional Deep Neural Network

Andrew JR Simpson, Gerard Roma, Mark D Plumbley

Research output: Chapter in Book/Report/Conference proceedingConference contribution

39 Citations (Scopus)

Abstract

Identification and extraction of singing voice from within musical mixtures is a key challenge in source separation and machine audition. Recently, deep neural networks (DNN) have been used to estimate ‘ideal’ binary masks for carefully controlled cocktail party speech separation problems. However, it is not yet known whether these methods are capable of generalizing to the discrimination of voice and non-voice in the context of musical mixtures. Here, we trained a convolutional DNN (of around a billion parameters) to provide probabilistic estimates of the ideal binary mask for separation of vocal sounds from real-world musical mixtures. We contrast our DNN results with more traditional linear methods. Our approach may be useful for automatic removal of vocal sounds from musical mixtures for ‘karaoke’ type applications.
Original languageEnglish
Title of host publicationLatent Variable Analysis and Signal Separation
EditorsEmmanuel Vincent, Arie Yeredor, Zbyněk Koldovský, Petr Tichavský
PublisherSpringer, Cham
Pages429-436
Number of pages8
ISBN (Electronic)9783319224824
ISBN (Print)9783319224817
DOIs
Publication statusPublished - 15 Aug 2015
Externally publishedYes
Event12th International Conference on Latent Variable Analysis and Signal Separation - Technical University of Liberec, Liberec, Czech Republic
Duration: 25 Aug 201528 Aug 2015
Conference number: 12

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume9237
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th International Conference on Latent Variable Analysis and Signal Separation
Abbreviated titleLVA/ICA 2015
CountryCzech Republic
CityLiberec
Period25/08/1528/08/15

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