Title: Automatic Music Highlight Extraction using Convolutional Recurrent Attention Networks,
Abstract: Music highlights are valuable contents for music services. Most methods
focused on low-level signal features. We propose a method for extracting
highlights using high-level features from convolutional recurrent attention
networks (CRAN). CRAN utilizes convolution and recurrent layers for sequential
learning with an attention mechanism. The attention allows CRAN to capture
significant snippets for distinguishing between genres, thus being used as a
high-level feature. CRAN was evaluated on over 32,000 popular tracks in Korea
for two months. Experimental results show our method outperforms three baseline
methods through quantitative and qualitative evaluations. Also, we analyze the
effects of attention and sequence information on performance.