Please use this identifier to cite or link to this item:
https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/4157
Title: | A Robust Approach to Predict the Popularity of Songs by Identifying Appropriate Properties |
Authors: | Anjana, S. |
Keywords: | Information Retrieval Machine Learning Algorithms |
Issue Date: | 19-Jul-2021 |
Abstract: | Hit Song Science is a major research topic which is being discussed today in the eld of Music Information Retrieval. In order to identify and predict whether a song could be a hit song or not is yet a challenging task. This thesis investigates the ability of using machine learning to make predictions on Sinhala songs whether they would be a hit or not. More than 13,000 Sinhala songs were collected by web scraping in a popular Sinhala music website which also contributes by presenting a dataset that can be used for further research purposes. The number of downloads and the view counts were used to derive a equation to measure the popularity. The features extracted of each song is used by the XGBoost classi cation algorithm. The songs are initially grouped into 3 classes based on their popularity, and later by performing machine learning algorithms on a set of features extracted on each song, the impact of the Linear Predictive Coding (LPC) overall average (LOA) and Mel-Frequency Cepstral Coe cients (MFCC) features, to the end result is indicated by the use of SHAP process. |
URI: | http://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/4157 |
Appears in Collections: | 2019 |
Files in This Item:
File | Description | Size | Format | |
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2015 CS 013.pdf | 512.57 kB | Adobe PDF | View/Open |
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