DCASE-models: a Python library for computational environmental sound analysis using deep-learning models

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• dc.contributor.author Zinemanas, Pablo
• dc.contributor.author Hounie, Ignacio
• dc.contributor.author Cancela, Pablo
• dc.contributor.author Font Corbera, Frederic
• dc.contributor.author Rocamora, Martín
• dc.contributor.author Serra, Xavier
• dc.date.accessioned 2020-11-02T14:11:28Z
• dc.date.available 2020-11-02T14:11:28Z
• dc.date.issued 2020
• dc.description Comunicació presentada a: 5th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2020) celebrat el 2 i 3 de novembre de 2020 a Tòquio, Japó.
• dc.description.abstract This document presents DCASE-models, an open–source Python library for rapid prototyping of environmental sound analysis systems, with an emphasis on deep–learning models. Together with a collection of functions for dataset handling, data preparation, feature extraction, and evaluation, it includes a model interface to standardize the interaction of machine learning methods with the other system components. This also provides an abstraction layer that allows the use of different machine learning backends. The package includes Python scripts, Jupyter Notebooks, and a web application, to illustrate its usefulness. The library seeks to alleviate the process of releasing and maintaining the code of new models, improve research reproducibility, and simplify comparison of methods. We expect it to become a valuable resource for the community.
• dc.format.mimetype application/pdf
• dc.identifier.citation Zinemanas P, Hounie I, Cancela P, Font F, Rocamora M, Serra X.DCASE-models: a Python library for computational environmental sound analysis using deep-learning models. In: Ono N, Harada N, Kawaguchi Y, Mesaros A, Imoto K, Koizumi Y, Komatsu T, editors. Proceedings of the Fifth Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2020); 2020 Nov 2-3; Tokyo, Japan. [Tokyo]: DCASE; 2020. p. 240-4. DOI: 10.5281/zenodo.4061782
• dc.identifier.doi http://dx.doi.org/10.5281/zenodo.4061782
• dc.identifier.isbn 978-4-600-00566-5
• dc.identifier.uri http://hdl.handle.net/10230/45641
• dc.language.iso eng
• dc.publisher Detection and Classication of Acoustic Scenes and Events (DCASE)
• dc.relation.ispartof Ono N, Harada N, Kawaguchi Y, Mesaros A, Imoto K, Koizumi Y, Komatsu T, editors. Proceedings of the Fifth Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2020); 2020 Nov 2-3; Tokyo, Japan. [Tokyo]: DCASE; 2020.
• dc.relation.isreferencedby https://github.com/MTG/DCASE-models
• dc.rights This work is licensed under a Creative Commons Attribution 4.0 International License.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Python library
• dc.subject.keyword Deep learning
• dc.subject.keyword Audio classification
• dc.subject.keyword Sound event detection
• dc.subject.keyword Reproducibility
• dc.title DCASE-models: a Python library for computational environmental sound analysis using deep-learning models
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/publishedVersion