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

Simpson, AR, Roma, G and Plumbley, M (2015) Deep Karaoke: Extracting Vocals from Musical Mixtures Using a Convolutional Deep Neural Network In: Latent Variable Analysis and Signal Separation: 12th International Conference, LVA/ICA 2015, 25-28 Aug 2015, Liberec, Czech Republic.

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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.

Item Type:	Conference or Workshop Item (Conference Paper)
Subjects :	Signal processing
Divisions :	Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :	Name Email ORCID Simpson, AR Roma, G Plumbley, M
Date :	15 August 2015
Identification Number :	10.1007/978-3-319-22482-4_50
Contributors :	Contribution Name Email ORCID http://www.loc.gov/loc.terms/relators/EDT Vincent, E http://www.loc.gov/loc.terms/relators/EDT Yeredor, A http://www.loc.gov/loc.terms/relators/EDT Koldovský, Z http://www.loc.gov/loc.terms/relators/EDT Tichavský, P http://www.loc.gov/loc.terms/relators/PBL Springer International Publishing,
Uncontrolled Keywords :	Deep learning, Supervised learning, Convolution, Source separation
Related URLs :	Author
Additional Information :	The original publication is available at http://link.springer.com/chapter/10.1007/978-3-319-22482-4_50
Depositing User :	Symplectic Elements
Date Deposited :	02 Feb 2016 09:01
Last Modified :	31 Oct 2017 17:59
URI:	http://epubs.surrey.ac.uk/id/eprint/809734

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