MuMu is a Multimodal Music dataset with multi-label genre annotations that combines information from the Amazon Reviews dataset and the Million Song Dataset (MSD). The former contains millions of album customer reviews and album metadata gathered from Amazon.com. The latter is a collection of metadata and precomputed audio features for a million songs. To map the information from both datasets we use MusicBrainz. This process yields the final set of 147,295 songs, which belong to 31,471 albums. For the mapped set of albums, there are 447,583 customer reviews from the Amazon Dataset. The dataset have been used for multi-label music genre classification experiments in the related publication. In addition to genre annotations, this dataset provides further information about each album, such as genre annotations, average rating, selling rank, similar products, and cover image url. For every text review it also provides helpfulness score of the reviews, average rating, and summary of the review. The mapping between the three datasets (Amazon, MusicBrainz and MSD), genre annotations, metadata, data splits, text reviews and links to images are available here. Images and audio files can not be released due to copyright issues. MuMu dataset (mapping, metadata, annotations and text reviews) Data splits and multimodal feature embeddings for ISMIR multi-label classification experiments These data can be used together with the Tartarus deep learning library https://github.com/sergiooramas/tartarus. NOTE: This version provides simplified files with metadata and splits. Scientific References Please cite the following papers if using MuMu dataset or Tartarus library. Oramas, S., Barbieri, F., Nieto, O., and Serra, X (2018). Multimodal Deep Learning for Music Genre Classification, Transactions of the International Society for Music Information Retrieval, V(1). Oramas S., Nieto O., Barbieri F., & Serra X. (2017). Multi-label Music Genre Classification from audio, text and images using Deep Features. In Proceedings of the 18th International Society for Music Information Retrieval Conference (ISMIR 2017). https://arxiv.org/abs/1707.04916

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).

{"references": ["Oramas et al. (2017). Dataset associated to https://arxiv.org/abs/1707.04916"]}