Centroid-model based music similarity with alpha divergence
알파 다이버전스를 이용한 무게중심 모델 기반 음악 유사도

Jin Soo Seo, Jeonghyun Kim, Jihyun Park
2016 The Journal of the Acoustical Society of Korea  
Music-similarity computation is crucial in developing music information retrieval systems for browsing and classification. This paper overviews the recently-proposed centroid-model based music retrieval method and applies the distributional similarity measures to the model for retrieval-performance evaluation. Probabilistic distance measures (also called divergence) compute the distance between two probability distributions in a certain sense. In this paper, we consider the alpha divergence in
more » ... omputing distance between two centroid models for music retrieval. The alpha divergence includes the widely-used Kullback-Leibler divergence and Bhattacharyya distance depending on the values of alpha. Experiments were conducted on both genre and singer datasets. We compare the music-retrieval performance of the distributional similarity with that of the vector distances. The experimental results show that the alpha divergence improves the performance of the centroid-model based music retrieval.
doi:10.7776/ask.2016.35.2.083 fatcat:ob7clhcdufhh7fbt3yxrizauy4