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ZENODO
Dataset . 2022
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2022
License: CC BY
Data sources: ZENODO
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ZENODO
Dataset . 2022
License: CC BY
Data sources: Datacite
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https://doi.org/10.5281/zenodo...
Dataset . 2022
License: CC BY
Data sources: Sygma
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LatentJam Dataset

Authors: Tovstogan, Philip; Serra, Xavier; Bogdanov, Dmitry;

LatentJam Dataset

Abstract

This LatentJam dataset contains 12 music latent representations (9 content-based and 3 collaborative-filtering) for 29 275 music tracks from Jamendo. It is released as part of the publication "Similarity of Nearest-Neighbor Query Results in Deep Latent Spaces". For more details on the individual models used for extraction, refer to the text of the paper. The example code that uses this dataset and reproduces the experiments and analysis done in the paper is available at https://github.com/philtgun/compare-embeddings. See the README for more details on how to use this dataset and individual files. The content-based representations have been extracted with the Essentia library (essentia-tensorflow 2.1b6.dev374) during the internship of the first author in the Jamendo in 2021 as part of the MIP-Frontiers project. The collaborative filtering representations have been computed from data provided by Jamendo with the Implicit library (AlternatingLeastSquares algorithm, implicit 0.4.4). The Python version used is 3.7.13. This dataset is released under CC BY-NC-SA 4.0 License. Please cite the publication if you use this dataset: @inproceedings{tovstogan_similarity_2009, title = {Similarity of nearest-neighbor query results in deep latent spaces}, author = {Tovstogan, Philip and Serra, Xavier and Bogdanov, Dmitry}, booktitle = {Proceedings of the 19th Sound and Music Computing Conference ({SMC})}, year = {2022} }

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Keywords

latent space, auto-tagging, collaborative filtering, music information retrieval, embeddings

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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