Monoaural audio source separation using deep convolutional neural networks

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• dc.contributor.author Chandna, Pritishca
• dc.contributor.author Miron, Mariusca
• dc.contributor.author Janer Mestres, Jordica
• dc.contributor.author Gómez Gutiérrez, Emilia, 1975-ca
• dc.date.accessioned 2017-05-29T10:07:26Z
• dc.date.available 2017-05-29T10:07:26Z
• dc.date.issued 2017
• dc.description 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.sponsorship This work is partially supported by the Spanish Ministry of Economy and Competitiveness under CASAS project (TIN2015-70816-R).
• dc.format.mimetype application/pdfca
• 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.doi http://dx.doi.org/10.1007/978-3-319-53547-0_25
• dc.identifier.uri http://hdl.handle.net/10230/32187
• dc.language.iso eng
• dc.publisher Springerca
• dc.relation.ispartof 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.projectID info:eu-repo/grantAgreement/ES/1PE/TIN2015-70816-R
• dc.rights © Springer The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-53547-0_25
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Convolutional autoencoder
• dc.subject.keyword Music source separation
• dc.subject.keyword Deep learning
• dc.subject.keyword Convolutional neural networks
• dc.subject.keyword Low-latency
• dc.title Monoaural audio source separation using deep convolutional neural networksca
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/acceptedVersion