MediaEval 2015: A Segmentation-based Approach to Continuous Emotion Tracking
Anna Aljanaki, Frans Wiering, Remco C. Veltkamp · MediaEval · 2015
In this paper we approach the task of continuous music emotion recognition using unsupervised audio segmentation as a preparatory step. The MediaEval task requires predicting emotion of the song with a high time resolution of 2Hz. Though this resolution is necessary to nd exact locations of emotional changes, we believe that those changes occur more sparsely. We suggest that using bigger time windows for feature extraction and emotion prediction might make emotion recognition more accurate. We use an unsupervised method Structure Features [6] to segment the audio both from the development set and the evaluation set. Then we use Gaussian Process regression to predict the emotion of the segment using features extracted with the Essentia and openSMILE frameworks.