Title:
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Multi-label music genre classification from audio, text and images using deep features
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Author:
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Oramas, Sergio; Nieto Caballero, Oriol; Barbieri, Francesco; Serra, Xavier
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Abstract:
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Comunicació presentada a la ISMIR 2017: 18th International Society for Music Information Retrieval Conference, celebrada els dies 23 a 27 d'octubre de 2017 a Suzhou, Xina. |
Abstract:
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Music genres allow to categorize musical items that share
common characteristics. Although these categories are not
mutually exclusive, most related research is traditionally
focused on classifying tracks into a single class. Furthermore,
these categories (e.g., Pop, Rock) tend to be too
broad for certain applications. In this work we aim to expand
this task by categorizing musical items into multiple
and fine-grained labels, using three different data modalities:
audio, text, and images. To this end we present
MuMu, a new dataset of more than 31k albums classified
into 250 genre classes. For every album we have collected
the cover image, text reviews, and audio tracks. Additionally,
we propose an approach for multi-label genre classification
based on the combination of feature embeddings
learned with state-of-the-art deep learning methodologies.
Experiments show major differences between modalities,
which not only introduce new baselines for multi-label
genre classification, but also suggest that combining them
yields improved results. |
Abstract:
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This work was partially funded by the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502). |
Subject(s):
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-Formes musicals -Classificació automàtica |
Rights:
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© Sergio Oramas, Oriol Nieto, Francesco Barbieri, Xavier Serra. Licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Attribution: Sergio Oramas, Oriol Nieto, Francesco Barbieri, Xavier Serra. “Multi-label Music Genre Classification from audio, text, and images using Deep Features”, 18th International Society for Music Information Retrieval Conference, Suzhou, China, 2017.
http://creativecommons.org/licenses/by/4.0/ |
Document type:
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Conference Object Article - Published version |
Published by:
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International Society for Music Information Retrieval (ISMIR)
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