RESEARCH ARTICLE


Iterative K-Line Meshing Non-Linear Least Squares Interpolation of Affectively Decorated Media Repositories



Anestis A. Toptsis*, Alexander Dubitski
Department of Computer Science and Engineering, York University, Toronto, Ontario, M3J 1P3, Canada.


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© 2017 Toptsis et al.;

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Department of Computer Science and Engineering, York University, Toronto, Ontario, M3J 1P3, Canada; Tel: (416) 736-2100, Ext. 66675; E-mail: anestis.toptsis@gmail.com; anestis@cse.yorku.ca


Abstract

We present an algorithm that organizes a song repository upon recording a user’s memory experiences from previous music listening activities. Our method forms an affectively annotated network of songs. The network’s connections correspond to a person’s recorded memory experiences related to song preferences when the person is at different states of affective bias. Upon formation of this network, an intelligent affect-sensitive network navigation algorithm synthesizes playlists that conform to desired affective states. The method for the network formation is highly individualized, in the sense that it takes in account an individual’s music preferences which are typically subjective and may differ from user to user. Also, the method is content independent, in the sense that it does not rely or favor any particular music genre. In fact, the method is applicable to any type of media, not only songs. We implement our method and present evaluation results from the introspection of our algorithms’ execution and from feedback recorded during the evaluation by human test subjects. The evaluation results clearly indicate that the proposed method significantly outperforms the most typical paradigm of random song selection.