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Multi-label Music Genre Classification from Audio, Text and Images Using Deep Features

Title Multi-label Music Genre Classification from Audio, Text and Images Using Deep Features
Publication Type Conference Paper
Year of Publication 2017
Conference Name 18th International Society for Music Information Retrieval Conference (ISMIR 2017)
Authors Oramas, S. , Nieto O. , Barbieri F. , & Serra X.
Conference Start Date 23/10/2017
Conference Location Suzhou, China
Abstract 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.
preprint/postprint document https://arxiv.org/abs/1707.04916
Additional material:
The MuMu dataset https://doi.org/10.5281/zenodo.831189 Tartarus deep learning code https://github.com/sergiooramas/tartarus