Título:
DCASE-models : A Python library for computational environmental sound analysis using deep-learning models.
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Director:
Compilador:
Autor:
Zinemanas, Pablo
Hounie, Ignacio
Cancela, Pablo
Font, Frederic
Rocamora, Martín
Serra, Xavier
Hounie, Ignacio
Cancela, Pablo
Font, Frederic
Rocamora, Martín
Serra, Xavier
Tutor:
Tipo de documento:
Ponencia
Editor:
Palabras clave:
Python library
Deep learning
Audio classification
Sound event detection
Reproducibility
Deep learning
Audio classification
Sound event detection
Reproducibility
Descriptores:
Año de publicación:
2020
Contenido:
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.
Descripción:
metadata.articulos.dc.description.uri:
Editorial:
DCASE
EN:
Detection and Classification of Acoustic Scenes and Events, DCASE 2020, Tokyo, Japan, 2-3 nov. 2020, pp. 1-5.
Financiadores:
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.
Citación:
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ISBN:
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Cobertura geográfica:
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Departamento académico:
Procesamiento de Señales
Grupo de investigación:
Procesamiento de Audio (GPA)
Licencia:
Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
Colecciones:
| Ficheros | Descripción | Tamaño | Formato | ||
|---|---|---|---|---|---|
| ZHCFRS20.pdf | Versión publicada | 211.18 KB | Adobe PDF |