@inproceedings{8e3128ed95f047ffb286f5ceb317caf4,
title = "Prediction of Song Popularity Using Machine Learning Concepts",
abstract = "Music plays an essential role in the well-being of many people. It can be therapeutic, motivational and can even unite people. However, not every song has the power to unite large groups of people and make them forget their differences. Each song that is universally acclaimed by the musically proficient and the masses alike has to have certain features that are similar. The aim of this work is to predict if a song will be universally recognized using features and attributes of that song which can be quantified using machine learning. The techniques which provide best accuracy are presented. The application finds use in playing relevant songs to the gathering, enhancement of mood.",
author = "Kaneria, \{Adit V.\} and Rao, \{Abishek B.\} and Aithal, \{Shivani G.\} and Pai, \{Smitha N.\}",
note = "Publisher Copyright: {\textcopyright} 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.",
year = "2021",
doi = "10.1007/978-981-16-0336-5\_4",
language = "English",
isbn = "9789811603358",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "35--48",
editor = "\{K V\}, Santhosh and Rao, \{K. Guruprasad\}",
booktitle = "Smart Sensors Measurements and Instrumentation - Select Proceedings of CISCON 2020",
address = "Germany",
}