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DC Field | Value | Language |
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dc.contributor.author | Anjana, S. | - |
dc.date.accessioned | 2021-07-19T07:43:47Z | - |
dc.date.available | 2021-07-19T07:43:47Z | - |
dc.date.issued | 2021-07-19 | - |
dc.identifier.uri | http://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/4157 | - |
dc.description.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. | en_US |
dc.language.iso | en | en_US |
dc.subject | Information Retrieval | en_US |
dc.subject | Machine Learning | en_US |
dc.subject | Algorithms | en_US |
dc.title | A Robust Approach to Predict the Popularity of Songs by Identifying Appropriate Properties | en_US |
dc.type | Thesis | en_US |
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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