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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 | ||
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ZHCFRS20.pdf | Versión publicada | 211,18 kB | Adobe PDF | Visualizar/Abrir |
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