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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 connec- tions 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 syn- thesizes 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.

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