Title:	APPLYING DATA MINING AND DATA VISUALIZATION WITHIN THE SCOPE OF AUDIO DATA USING SPOTIFY
Author(s):	Marika Apostolova Trpkovska, Arbesa Kajtazi, Lejla Abazi Bexheti and Arbana Kadriu
ISBN:	978-989-8533-87-6
Editors:	Miguel Baptista Nunes, Pedro Isaías, Philip Powell, Pascal Ravesteijn and Guido Ongena
Year:	2019
Edition:	Single
Keywords:	Data Mining, Data Visualization, Audio Data, Patterns, Spotify
Type:	Full Paper
First Page:	197
Last Page:	204
Language:	English
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Full Contents:	click to dowload
Paper Abstract:	The aim of this research is to put forward an overview of applying data mining and data visualization within the scope of audio data from a dataset of Spotify. The research starts by presenting background information of these two fields and their influence on music industry. The paper includes explanation of the most essential concepts and their role. The research is concentrated on analysis of audio features of the tracks of Spotify’s Top Songs in 2017 playlist and tries to highlight the common patterns behind the audio features of these songs. For the purposes of this research, Spotify datasets are used as practical scenario. For this reason, more detailed information is given about songs features, what are they, what do these top songs have in common and why do people like them. The result of the study showcase how singers and song makers can leverage the power of data visualization and data mining to help trying to predict one audio feature based on the others, look for patterns in the audio features of the songs and see which features correlate the most.
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