The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/21981442

Jaehun Kim; Liem, Cynthia (2023): Supplementary material of the paper "The power of deep without going deep? A study of HDPGMM music representation learning". Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/21981442.v1

Dataset

• Artificial Intelligence and Image Processing
• Other Information and Computing Sciences
• Information Systems

Gaussian Mixture Models, hierarchical dirichlet process(HDP), Million Song Dataset, nonparametric Bayesian modeling technique, Representation Learning

CC BY-SA 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Jaehun Kim , Cynthia Liem

Supplementary material of the paper "The power of deep without going deep? A study of HDPGMM music representation learning"

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Authors:

    Jaehun Kim (jaehun.j.kim@gmail.com)

    Cynthia C.S. Liem

# General Information

This entry contains the following list of data that is the by-product of the experiment conducted for a study titled "[The power of deep without going deep? A study of HDPGMM music representation learning](https://zenodo.org/record/7316610#.Y9xjoS-B0Q0)". In addition, the program for the main experimental routine is provided in the [separate repository](https://github.com/eldrin/hdpgmm-music-experiments).

• 2023-02-06 first online, published, posted

4TU.ResearchData

g-zipped file contains various file formats including '*.h5', '*.npz', and '*.csv'

The power of deep without going deep? A study of HDPGMM music representation learning

TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Intelligent Systems