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CloserMusicDB: A Modern Multipurpose Dataset of High Quality Music

Aleksandra Piekarzewicz, Tomasz Sroka, Aleksander Tym, Mateusz Modrzejewski

TL;DR

CloserMusicDB, a collection of full length studio quality tracks annotated by a team of human experts, is introduced, along with three example tasks possible to perform using this dataset: hook detection, contextual tagging and artist identification.

Abstract

In this paper, we introduce CloserMusicDB, a collection of full length studio quality tracks annotated by a team of human experts. We describe the selected qualities of our dataset, along with three example tasks possible to perform using this dataset: hook detection, contextual tagging and artist identification. We conduct baseline experiments and provide initial benchmarks for these tasks.

CloserMusicDB: A Modern Multipurpose Dataset of High Quality Music

TL;DR

CloserMusicDB, a collection of full length studio quality tracks annotated by a team of human experts, is introduced, along with three example tasks possible to perform using this dataset: hook detection, contextual tagging and artist identification.

Abstract

In this paper, we introduce CloserMusicDB, a collection of full length studio quality tracks annotated by a team of human experts. We describe the selected qualities of our dataset, along with three example tasks possible to perform using this dataset: hook detection, contextual tagging and artist identification. We conduct baseline experiments and provide initial benchmarks for these tasks.

Paper Structure

This paper contains 11 sections, 2 figures.

Table of Contents

  1. Introduction
  2. Example tasks
  3. Hook detection
  4. Contextual tagging
  5. Artist identification
  6. Dataset structure
  7. Initial benchmarks
  8. Hook detection
  9. Contextual tagging and artist identification
  10. License
  11. Conclusions

Figures (2)

  • Figure 1: Fifty most frequent music hashtags in the CloserMusicDB.
  • Figure :