Musical perceptual similarity estimation using interactive genetic algorithm

This paper proposes a new approach to estimate the emotional perceptual similarity of music using interactive genetic algorithm. Different combinations of measure function and feature weights construct the searching space, and users´ subjective similarity evaluations of musical pieces are used as the fitness. The approach tries to search for the optimal combination of measure function and feature weights to better reflect human´s perception. A comparative emotion detection experiment is designed to explore the effectiveness of our approach in our MIDI database. The one is the simple K-Nearest Neighbor (KNN) and the other is the modified KNN using the optimal combination obtained. Experimental results show that our method outperforms the simple KNN classifier, which confirms its usefulness of better reflecting human´s perception.