A Deep Music Recommendation Method Based on Human Motion Analysis

Abstract

Despite its applications in automatic video editing and automatic music composition, the problem of music recommendation from dance motions has seldom been explored. In order to solve this problem, this work proposes a deep music recommendation algorithm based on dance motion analysis and evaluate it through quantitative measures. For quantitative evaluation, this work implements a LSTM-AE based music recommendation method which learns the correspondences between motion and music. In experiments, the two methodologies are compared and the motion analysis based methods outperform their rival by large margins. This work also proposes a quantitative measure of accurately recommended music genre. The proposed motion analysis based method achieves a recommendation accuracy of 91.3% using late fusion of joint and limb features.