Popular song summarization using chorus section detection from audio signal

Music signal is a one-dimensional temporal sequence. It thus incurs difficulty for the listeners to quickly capturing the mostly attracting parts in popular songs, unless the listeners play the song until the ending. In order to improve the listening experience, music summarization, a tool to summarize the song using the most attractive sections, is needed. In the paper, a system and method is presented to summarize the popular songs by detecting the chorus sections from the input audio signal. The proposed summarization system uses the unique audio feature representation method, i.e. the octave-dependent probabilistic latent semantic analysis, and the chorus detection algorithm that combines the repeated segment extraction and chorus identification. The performance of music summarization is evaluated on the song database with the ground truth of chorus sections, i.e. the start and ending time-stamp of each chorus section. As we know, it is the first systematically evaluation of music summary performance. In terms of multiple metrics such as the boundary accuracy, precision, recall and F1, we show that the proposed system is much superior to the widely accepted methods.