TY  - JOUR
AU  - Bengani, Shaleen 
AU  - Vadivel, S. 
AU  - Arul Jothi, J. Angel 
PY  - 2019
TI  - Efficient Music Auto-Tagging with Convolutional Neural Networks
JF  - Journal of Computer Science
VL  - 15
IS  - 8
DO  - 10.3844/jcssp.2019.1203.1208
UR  - https://thescipub.com/abstract/jcssp.2019.1203.1208
AB  - 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.