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dc.contributor.author Alonso-Jiménez, Pablo
dc.contributor.author Bogdanov, Dmitry
dc.contributor.author Serra, Xavier
dc.date.accessioned 2020-10-09T07:34:46Z
dc.date.available 2020-10-09T07:34:46Z
dc.date.issued 2020
dc.identifier.citation Alonso-Jiménez P, Bogdanov D, Serra X. Deep embeddings with Essentia models. Paper presented at: International Society of Music Information Retrieval Conference (ISMIR); 2020 Oct 11-16; Montréal, Canada.
dc.identifier.uri http://hdl.handle.net/10230/45452
dc.description Comunicació presentada a: International Society for Music Information Retrieval Conference celebrat de l'11 al 16 d'octubre de 2020 de manera virtual.
dc.description.abstract We present the integration of various CNN TensorFlow models developed for different MIR tasks into Essentia. This is a continuation of our previous work [1], extending the list of supported models and adding new algorithms to facilitate usability. Essentia provides input feature extraction and inference with TensorFlow models in a single C++ pipeline with Python bindings, facilitating the deployment of C++ and Python MIR applications. We assess the new models’ capabilities to serve as embedding extractors in many downstream classification tasks. All presented models are publicly available on the Essentia website.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher ISMIR
dc.rights Licensed under a Creative Commons Attribution 4.0 In- ternational License (CC BY 4.0). 21st International Society for Music Information Retrieval Conference, Montréal, Canada, 2020.
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.title Deep embeddings with Essentia models
dc.type info:eu-repo/semantics/conferenceObject
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/acceptedVersion

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