Neural percussive synthesis parameterised by high-level timbral features

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• dc.contributor.author Ramires, António
• dc.contributor.author Chandna, Pritish
• dc.contributor.author Favory, Xavier
• dc.contributor.author Gómez Gutiérrez, Emilia, 1975-
• dc.contributor.author Serra, Xavier
• dc.date.accessioned 2021-02-12T07:23:18Z
• 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 neural network-based methodology for synthesising percussive sounds with control over high-level timbral characteristics of the sounds. This approach allows for intuitive control of a synthesizer, enabling the user to shape sounds without extensive knowledge of signal processing. We use a feedforward convolutional neural network-based architecture, which is able to map input parameters to the corresponding waveform. We propose two datasets to evaluate our approach on both a restrictive context, and in one covering a broader spectrum of sounds. The timbral features used as parameters are taken from recent literature in signal processing. We also use these features for evaluation and validation of the presented model, to ensure that changing the input parameters produces a congruent waveform with the desired characteristics. Finally, we evaluate the quality of the output sound using a subjective listening test. We provide sound examples and the system's source code for reproducibility.en
• dc.description.sponsorship This work is partially funded by the European Unions Horizon 2020 research and innovation programme under the Marie Skodowska-Curie grant agreement No765068, MIP-Frontiers. This work is partially supported by the Towards Richer Online Music Public-domain Archives (TROMPA) project. The TITANX used for this research was donated by the NVIDIA Corporation.
• dc.format.mimetype application/pdf
• dc.identifier.citation Ramires A, Chandna P, Favory X, Gómez E, Serra X. Neural percussive synthesis parameterised by high-level timbral features. 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. 786-90. DOI: 10.1109/ICASSP40776.2020.9053128
• dc.identifier.doi http://dx.doi.org/10.1109/ICASSP40776.2020.9053128
• dc.identifier.issn 2379-190X
• dc.identifier.uri http://hdl.handle.net/10230/46458
• 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. 786-90
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/765068
• 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/ICASSP40776.2020.9053128
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Wave-U-Neten
• dc.subject.keyword Percussive sound synthesisen
• dc.subject.keyword Generative modelsen
• dc.subject.keyword Music information retrievalen
• dc.subject.keyword Creative interfacesen
• dc.title Neural percussive synthesis parameterised by high-level timbral featuresen
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/acceptedVersion