Research Article Open Access

Efficient Music Auto-Tagging with Convolutional Neural Networks

Shaleen Bengani1, S. Vadivel1 and J. Angel Arul Jothi1
  • 1 Birla Institute of Technology and Science Pilani Dubai Campus, United Arab Emirates


Technology is revolutionizing the way in which music is distributed and consumed. As a result, millions of songs are instantly available to millions of people, on the Internet. This has created the need for novel music search and discovery services. Music is often searched using descriptive keywords, or tags, based on the content of the song. Hence, one very important task in achieving a great music search engine is automatic tagging of music. Currently, deep learning techniques using convolutional neural networks produce state- of-the-art results for this task. Several deep learning algorithms are able to achieve good results but at the cost of efficiency. As neural networks get deeper, the cost of computation grows exponentially. In this paper, we present a deep learning-based ensemble method that achieves near state-of-the-art performance on the music auto-tagging task. Our method is significantly more efficient in terms of computation time and disk space. This opens up the option of using our proposed model directly on a mobile device.

Journal of Computer Science
Volume 15 No. 8, 2019, 1203-1208


Submitted On: 27 April 2019 Published On: 27 August 2019

How to Cite: Bengani, S., Vadivel, S. & Arul Jothi, J. A. (2019). Efficient Music Auto-Tagging with Convolutional Neural Networks. Journal of Computer Science, 15(8), 1203-1208.

  • 3 Citations



  • Deep Learning
  • Convolutional Neural Network
  • Music Auto-Tagging
  • Mel Spectrogram