Content based singing voice extraction from a musical mixture

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• dc.contributor.author Chandna, Pritish
• dc.contributor.author Blaauw, Merlijn
• dc.contributor.author Bonada, Jordi, 1973-
• dc.contributor.author Gómez Gutiérrez, Emilia, 1975-
• dc.date.accessioned 2021-02-12T07:23:11Z
• dc.date.issued 2020
• dc.description Comunicació presentada a: ICASSP 2020 IEEE International Conference on Acoustics, Speech and Signal Processing, celebrat en línia del 4 al 8 de maig de 2020.
• dc.description.abstract We present a deep learning based methodology for extracting the singing voice signal from a musical mixture based on the underlying linguistic content. Our model follows an encoder-decoder architecture and takes as input the magnitude component of the spectrogram of a musical mixture with vocals. The encoder part of the model is trained via knowledge distillation using a teacher network to learn a content embedding, which is decoded to generate the corresponding vocoder features. Using this methodology, we are able to extract the unprocessed raw vocal signal from the mixture even for a processed mixture dataset with singers not seen during training. While the nature of our system makes it incongruous with traditional objective evaluation metrics, we use subjective evaluation via listening tests to compare the methodology to state-of-the-art deep learning based source separation algorithms. We also provide sound examples and source code for reproducibility.en
• dc.description.sponsorship This work is partially supported by the Towards Richer Online Music Public-domain Archives (TROMPA H2020 770376) project.
• dc.format.mimetype application/pdf
• dc.identifier.citation Chandna P, Blaauw M, Bonada J, Gómez E. Content based singing voice extraction from a musical mixture. In: 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP); 2020 May 4-8; Barcelona, Spain. New Jersery: The Institute of Electrical and Electronics Engineers; 2020. p. 781-85. DOI: 10.1109/ICASSP40776.2020.9053024
• dc.identifier.doi http://dx.doi.org/10.1109/ICASSP40776.2020.9053024
• dc.identifier.issn 2379-190X
• dc.identifier.uri http://hdl.handle.net/10230/46456
• dc.language.iso eng
• dc.publisher Institute of Electrical and Electronics Engineers (IEEE)
• dc.relation.ispartof 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP); 2020 May 4-8; Barcelona, Spain. New Jersery: The Institute of Electrical and Electronics Engineers; 2020. p. 781-85
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/770376
• 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. http://dx.doi.org/10.1109/ICASSP40776.2020.9053024
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Source separationen
• dc.subject.keyword Singing voiceen
• dc.subject.keyword Content disentanglingen
• dc.subject.keyword Knowledge distillationen
• dc.subject.keyword AutoVCen
• dc.title Content based singing voice extraction from a musical mixtureen
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/acceptedVersion