Monoaural audio source separation using deep convolutional neural networks

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dc.contributor.author	Chandna, Pritish
dc.contributor.author	Miron, Marius
dc.contributor.author	Janer Mestres, Jordi
dc.contributor.author	Gómez Gutiérrez, Emilia, 1975-
dc.date	2017
dc.identifier.citation	Chandna P, Miron M, Janer J, Gómez E. Monoaural audio source separation using deep convolutional neural networks. In: Tichavsky P, Babaie-Zadeh M, Michel OJ, Thirion-Moreau N, editors. Latent variable analysis and signal separation. 13th International Conference, LVA/ICA 2017; 2017 Feb 21-23; Grenoble, France. [place unknown]: Springer; 2017. p. 258-66. DOI: 10.1007/978-3-319-53547-0_25
dc.identifier.citation	http://dx.doi.org/10.1007/978-3-319-53547-0_25
dc.identifier.uri	http://hdl.handle.net/10230/32187
dc.format	application/pdf
dc.language.iso	eng
dc.publisher	Springer
dc.relation	Tichavsky P, Babaie-Zadeh M, Michel OJ, Thirion-Moreau N, editors. Latent variable analysis and signal separation. 13th International Conference, LVA/ICA 2017; 2017 Feb 21-23; Grenoble, France. [place unknown]: Springer; 2017. p. 258-66.
dc.relation	info:eu-repo/grantAgreement/ES/1PE/TIN2015-70816-R
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	© Springer The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-53547-0_25
dc.subject	Convolutional autoencoder
dc.subject	Music source separation
dc.subject	Deep learning
dc.subject	Convolutional neural networks
dc.subject	Low-latency
dc.title	Monoaural audio source separation using deep convolutional neural networks
dc.type	info:eu-repo/semantics/conferenceObject
dc.type	info:eu-repo/semantics/acceptedVersion
dc.description.abstract	Comunicació presentada a 13th International Conference on Latent Variable Analysis and Signal Separation, celebrada a Grenoble (França) els dies 21 a 23 de febrer de 2017.
dc.description.abstract	In this paper we introduce a low-latency monaural source separation framework using a Convolutional Neural Network (CNN). We use a CNN to estimate time-frequency soft masks which are applied for source separation. We evaluate the performance of the neural network on a database comprising of musical mixtures of three instruments: voice, drums, bass as well as other instruments which vary from song to song. The proposed architecture is compared to a Multilayer Perceptron (MLP), achieving on-par results and a significant improvement in processing time. The algorithm was submitted to source separation evaluation campaigns to test efficiency, and achieved competitive results.
dc.description.abstract	This work is partially supported by the Spanish Ministry of Economy and Competitiveness under CASAS project (TIN2015-70816-R).

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