Knowledge Discovery in Musical Databases for Moods Detection
| Autor: | Andres Felipe Pinzon Baldion, German Rodriguez Mercado, José Rafael García González, Leidy Perez Coronell, David Garcia Herazo, Paola Andrea Sánchez, Jhonny Cano Zuluaga |
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| Rok vydání: | 2019 |
| Předmět: |
Data Analysis
0303 health sciences General Computer Science Artificial neural network Database Computer science 05 social sciences Musical Music player computer.software_genre 03 medical and health sciences Knowledge discovery Knowledge extraction Databases process State (computer science) 0509 other social sciences Electrical and Electronic Engineering Prediction 050904 information & library sciences Constant (mathematics) Data mining computer Music 030304 developmental biology |
| Zdroj: | IEEE LATIN AMERICA TRANSACTIONS Vol. 17, N°. 12 (2019) |
| ISSN: | 1548-0992 |
| DOI: | 10.1109/tla.2019.9011552 |
| Popis: | In this paper, methodology Knowledge discovery in databases is used in the design and implementation of a tool for moods detection from musical data. The application allows users to interact with a music player, and based on their playlist and musical genre, recognizes and classified their emotional state using a neural network. The results found are promising to have an accuracy of more than 72,4%, in addition the developed tool allows the constant taking and storage of data, the analysis in real time and issues suggestions of songs to positively influence the current emotional state, so that a greater use of the application can guarantee better results. |
| Databáze: | OpenAIRE |
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