Tensorflow audio models in Essentia

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• dc.contributor.author Alonso-Jiménez, Pablo
• dc.contributor.author Bogdanov, Dmitry
• dc.contributor.author Pons Puig, Jordi
• dc.contributor.author Serra, Xavier
• dc.date.accessioned 2021-02-12T07:23:07Z
• 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 Essentia is a reference open-source C ++ /Python library for audio and music analysis. In this work, we present a set of algorithms that employ TensorFlow in Essentia, allow predictions with pre-trained deep learning models, and are designed to offer flexibility of use, easy extensibility, and real-time inference. To show the potential of this new interface with TensorFlow, we provide a number of pre-trained state-of-the-art music tagging and classification CNN models. We run an extensive evaluation of the developed models. In particular, we assess the generalization capabilities in a cross-collection evaluation utilizing both external tag datasets as well as manual annotations tailored to the taxonomies of our models.en
• dc.format.mimetype application/pdf
• dc.identifier.citation Alonso-Jiménez P, Bogdanov D, Pons J, Serra X. Tensorflow audio models in Essentia. 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. 266-70. DOI: 10.1109/ICASSP40776.2020.9054688
• dc.identifier.doi http://dx.doi.org/10.1109/ICASSP40776.2020.9054688
• dc.identifier.issn 2379-190X
• dc.identifier.uri http://hdl.handle.net/10230/46455
• 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. 266-70
• 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/ICASSP40776.2020.9054688
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Music information retrievalen
• dc.subject.keyword Music taggingen
• dc.subject.keyword Deep learningen
• dc.subject.keyword Transfer learningen
• dc.subject.keyword Audio analysis softwareen
• dc.title Tensorflow audio models in Essentiaen
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/acceptedVersion