paper is UPDATED; Convolutional Recurrent Neural Networks for Music Classification (+reviews)

Summary

I updated my paper: arXiv link

Compare to the previous one, I

  • added one more architecture,
  • changed their names,
  • removed dropout from all convolution and fully-connected layers,
  • and re-ran all the experiments

Hopefully the figures and table below would be interesting enough to read the paper!

  • Those are layouts.

screen-shot-2016-11-03-at-22-11-11

  • In detail,

screen-shot-2016-11-03-at-22-11-20

  • Results are,

screen-shot-2016-11-03-at-22-11-34

  • The same results in time-AUC plane,

screen-shot-2016-11-03-at-22-11-38

  • Performances per tag, but this figure is better to be seen within the paper

screen-shot-2016-11-03-at-22-11-46

Reviews

Review 1

  • Importance/Relevance: Of sufficient interest
  • Novelty/Originality: Minor originality
  • Technical Correctness: Probably correct
  • Experimental Validation: Sufficient validation/theoretical paper
    • Comment on Experimental Validation:
      Experiments on a large music corpus were carried out thoroughly, where comparisons among three different models (Conv1D, Conv2D, and CRNN) were done for different combinations of the number of hidden layers and the number of parameters.
  • Clarity of Presentation: Clear enough
  • Reference to Prior Work: References adequate
  • General Comments to Authors:
    • This study considers automatic classification of music, and reports the results of the experiment where three different types of convolutional neural networks (Conv1D, Conv2D, and CRNN) were compared thoroughly.
      Although convolutional recurrent neural network (CRNN) is not a new model, the evaluation done in the study is solid, which provides useful information to the people working in the research area.

Review 2

  • Importance/Relevance: Of limited interest
    • Comment on Importance/Relevance:
      In my opinion the paper might be of interest only for people working specifically on music classification problem with CNNs.
  • Novelty/Originality: Minor originality
    • Comment on Novelty/Originality:
      The use of RNN in CNNs for music tagging seems to be a relatively simple extension of the existing methods. The improvement is also moderate.
  • Technical Correctness: Definitely correct
  • Experimental Validation: Limited but convincing
  • Clarity of Presentation: Clear enough
  • Reference to Prior Work: References adequate

Review 3

  • Importance/Relevance: Of sufficient interest
    • Comment on Importance/Relevance:
      investigating properties of deep learning is important, especially these days, across application domains
  • Novelty/Originality: Moderately original
    • Comment on Novelty/Originality:
      little novel technical contribution, but a solid and much-needed empirical study
  • Technical Correctness: Definitely correct
  • Experimental Validation: Sufficient validation/theoretical paper
  • Clarity of Presentation: Very clear
  • Reference to Prior Work: Excellent references
  • General Comments to Authors:
    • well done experimental study

My comments about reviews

  • TL;DR of the reviews would be “not too original, useful enough, nice experiment, good writing”, which I’m quite glad with. The paper is kinda suggesting CRNN, but also about benchmark/comparison (, which was the original title of the paper).
  • Surprised that the review 1 mentioned  http://www.cs.stanford.edu/people/anusha/static/deepplaylist.pdf. I knew it and wasn’t sure if I have to this for two reasons: it’s a school class project, and it’s not directly related more than ‘music’ x ‘ConvRNN’. I guess the reviewer searched while reviewing, which would make the review good!

 

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