Scopus Indexed Paper

Paper Details


Title
Recommendation Approach of English Songs Title Based on Latent Dirichlet Allocation applied on Lyrics
Abstract
The significance of music has evolved due to the vast diversity of entertainment industry. Songs are the widely used entertainment segment that can influence directly to the heart of the listeners. Choosing a suitable title for a song is considered as a common problem faced by the music directors. As the title gives the first impression of the song and only by the title listeners usually decide whether they will listen to this song or not, thus makes it a challenging task to determine. Lyrics are the most influential part of a particular song apart from the tune, rhythm, fusion, singer, genre etc. In this paper, we propose an approach to estimate and recommend the title of the song based on its lyrics. We have applied Latent Dirichlet Allocation (LDA) to find the hidden or implied topic of the song. The output of the LDA algorithm provides scoring on the significant words, which are passed to an estimation process to generate a song title. The proposed approach was experimented on over 200 English songs database having vast diversity in genre. The approach could be evaluated by the existing song title and the evaluation process is same as any recommendation system.
Keywords
Estimation, Resource management, Probabilistic logic, Data collection, Semantics, Industries
Authors
Rafayet Hossain, Md. Rahmatul Kabir Rasel Sarker, Mehejabin Mimo, Ahmed Al Marouf, Bishwajeet Pandey
Phone
Journal or Conference Name
Proceedings of 2019 3rd IEEE International Conference on Electrical, Computer and Communication Technologies, ICECCT 2019
Publish Year
2019
Indexing
Scopus