english Icono del idioma   español Icono del idioma  

Por favor, use este identificador para citar o enlazar este ítem: https://hdl.handle.net/20.500.12008/48400 Cómo citar
Título: DCASE-models : A Python library for computational environmental sound analysis using deep-learning models.
Autor: Zinemanas, Pablo
Hounie, Ignacio
Cancela, Pablo
Font, Frederic
Rocamora, Martín
Serra, Xavier
Tipo: Ponencia
Palabras clave: Python library, Deep learning, Audio classification, Sound event detection, Reproducibility
Fecha de publicación: 2020
Resumen: 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.
Editorial: DCASE
EN: Detection and Classification of Acoustic Scenes and Events, DCASE 2020, Tokyo, Japan, 2-3 nov. 2020, pp. 1-5.
Citación: Zinemanas, P., Hounie, I., Cancela, P. y otros. DCASE-models : A Python library for computational environmental sound analysis using deep-learning models [en línea]. EN: Detection and Classification of Acoustic Scenes and Events, DCASE 2020, Tokyo, Japan, 2-3 nov. 2020, pp. 1-5.
Departamento académico: Procesamiento de Señales
Grupo de investigación: Procesamiento de Audio (GPA)
Aparece en las colecciones: Publicaciones académicas y científicas - Instituto de Ingeniería Eléctrica

Ficheros en este ítem:
Fichero Descripción Tamaño Formato   
ZHCFRS20.pdfVersión publicada211,18 kBAdobe PDFVisualizar/Abrir


Este ítem está sujeto a una licencia Creative Commons Licencia Creative Commons Creative Commons