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

Andrew JR Simpson; Gerard Roma; Mark D Plumbley

doi:10.1007/978-3-319-22482-4_50

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 proceeding › Conference 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 language	English
Title of host publication	Latent Variable Analysis and Signal Separation
Editors	Emmanuel Vincent, Arie Yeredor, Zbyněk Koldovský, Petr Tichavský
Publisher	Springer, Cham
Pages	429-436
Number of pages	8
ISBN (Electronic)	9783319224824
ISBN (Print)	9783319224817
DOIs	https://doi.org/10.1007/978-3-319-22482-4_50
Publication status	Published - 15 Aug 2015
Externally published	Yes
Event	12th International Conference on Latent Variable Analysis and Signal Separation - Technical University of Liberec, Liberec, Czech Republic Duration: 25 Aug 2015 → 28 Aug 2015 Conference number: 12

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	9237
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th International Conference on Latent Variable Analysis and Signal Separation
Abbreviated title	LVA/ICA 2015
Country	Czech Republic
City	Liberec
Period	25/08/15 → 28/08/15

Access to Document

10.1007/978-3-319-22482-4_50Licence: Unspecified

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Simpson, A. JR., Roma, G., & Plumbley, M. D. (2015). Deep Karaoke: Extracting Vocals from Musical Mixtures Using a Convolutional Deep Neural Network. In E. Vincent, A. Yeredor, Z. Koldovský, & P. Tichavský (Eds.), Latent Variable Analysis and Signal Separation (pp. 429-436). (Lecture Notes in Computer Science; Vol. 9237). Springer, Cham. https://doi.org/10.1007/978-3-319-22482-4_50

@inproceedings{4c378b1010ab4456988ce6ee1f333c42,

title = "Deep Karaoke: Extracting Vocals from Musical Mixtures Using a Convolutional Deep Neural Network",

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 {\textquoteleft}ideal{\textquoteright} 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 {\textquoteleft}karaoke{\textquoteright} type applications.",

keywords = "Convolution, Deep learning, Source separation, Supervised learning",

author = "Simpson, {Andrew JR} and Gerard Roma and Plumbley, {Mark D}",

year = "2015",

month = aug,

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doi = "10.1007/978-3-319-22482-4_50",

language = "English",

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series = "Lecture Notes in Computer Science",

publisher = "Springer, Cham",

pages = "429--436",

editor = "Emmanuel Vincent and Arie Yeredor and Zbyn{\v e}k Koldovsk{\'y} and Petr Tichavsk{\'y}",

booktitle = "Latent Variable Analysis and Signal Separation",

address = "Switzerland",

note = "12th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2015 ; Conference date: 25-08-2015 Through 28-08-2015",

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Simpson, AJR, Roma, G & Plumbley, MD 2015, Deep Karaoke: Extracting Vocals from Musical Mixtures Using a Convolutional Deep Neural Network. in E Vincent, A Yeredor, Z Koldovský & P Tichavský (eds), Latent Variable Analysis and Signal Separation. Lecture Notes in Computer Science, vol. 9237, Springer, Cham, pp. 429-436, 12th International Conference on Latent Variable Analysis and Signal Separation, Liberec, Czech Republic, 25/08/15. https://doi.org/10.1007/978-3-319-22482-4_50

Deep Karaoke : Extracting Vocals from Musical Mixtures Using a Convolutional Deep Neural Network. / Simpson, Andrew JR; Roma, Gerard; Plumbley, Mark D.

Latent Variable Analysis and Signal Separation. ed. / Emmanuel Vincent; Arie Yeredor; Zbyněk Koldovský; Petr Tichavský. Springer, Cham, 2015. p. 429-436 (Lecture Notes in Computer Science; Vol. 9237).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - 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.

KW - Convolution

KW - Deep learning

KW - Source separation

KW - Supervised learning

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BT - Latent Variable Analysis and Signal Separation

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