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dc.contributor.authorGrais, Emad M.
dc.contributor.authorZhao, Fei
dc.contributor.authorPlumbley, Mark D.
dc.date.accessioned2021-05-10T10:38:15Z
dc.date.available2021-05-10T10:38:15Z
dc.date.issued2020-12-18
dc.identifier.citationGrais, Emad M., Zhao, Fei and Plumbley, Mark D. (2020) Multi-Band Multi-Resolution Fully Convolutional Neural Networks for Singing Voice Separation In: 28th European Signal Processing Conference (EUSIPCO 2020), 18-21 Jan 2021, Amsterdam, The Netherlands. https://doi.org/10.23919/Eusipco47968.2020.9287367en_US
dc.identifier.isbn978-9-0827-9705-3
dc.identifier.issn2076-1465
dc.identifier.urihttp://hdl.handle.net/10369/11392
dc.descriptionConference paper published in proceedings of the 28th European Signal Processing Conference (EUSIPCO 2020) available at https://doi.org/10.23919/Eusipco47968.2020.9287367en_US
dc.description.abstractDeep neural networks with convolutional layers usually process the entire spectrogram of an audio signal with the same time-frequency resolutions, number of filters, and dimensionality reduction scale. According to the constant-Q transform, good features can be extracted from audio signals if the low frequency bands are processed with high frequency resolution filters and the high frequency bands with high time resolution filters. In the spectrogram of a mixture of singing voices and music signals, there is usually more information about the voice in the low frequency bands than the high frequency bands. These raise the need for processing each part of the spectrogram differently. In this paper, we propose a multi-band multi-resolution fully convolutional neural network (MBR-FCN) for singing voice separation. The MBR-FCN processes the frequency bands that have more information about the target signals with more filters and smaller dimensionality reduction scale than the bands with less information. Furthermore, the MBR-FCN processes the low frequency bands with high frequency resolution filters and the high frequency bands with high time resolution filters. Our experimental results show that the proposed MBRFCN with very few parameters achieves better singing voice separation performance than other deep neural networks.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofseries2020 28th European Signal Processing Conference (EUSIPCO);
dc.rights© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
dc.titleMulti-Band Multi-Resolution Fully Convolutional Neural Networks for Singing Voice Separationen_US
dc.typeConference paperen_US
dc.identifier.doihttps://doi.org/10.23919/Eusipco47968.2020.9287367
rioxxterms.funderCardiff Metropolitan Universityen_US
rioxxterms.identifier.projectCardiff Metropolian (Internal)en_US
rioxxterms.versionAMen_US
rioxxterms.funder.project37baf166-7129-4cd4-b6a1-507454d1372een_US


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