Knowledge Discovery in Musical Databases for Moods Detection

Authors

  • Paola A. Sánchez-Sánchez Universidad Simón Bolívar, Barranquilla - Colombia
  • Jhonny Cano Universidad Simón Bolívar
  • David García Universidad Simón Bolívar
  • Andrés Pinzon Universidad Simón Bolívar
  • Germán Rodriguez Universidad Simón Bolívar
  • José Rafael García González Universidad Simón Bolívar
  • Leidy Haidy Perez Coronell Universidad Simón Bolívar

Keywords:

Data Mining, Knowledge discovery in databases Process, Music, Prediction and Data Analysis

Abstract

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 85%, 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.

Downloads

Download data is not yet available.

Downloads

Published

2020-02-16

How to Cite

Sánchez-Sánchez, P. A., Cano, J., García, D. ., Pinzon, A. ., Rodriguez, G. ., García González, J. R. ., & Perez Coronell, L. H. . (2020). Knowledge Discovery in Musical Databases for Moods Detection . IEEE Latin America Transactions, 17(12), 2061–2068. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/2359

Issue

Vol. 17 No. 12 (2019): Special Issue on Deep Learning

Section

Special Isssue on Deep Learning