Adaptive whitening for real-time music beat tracking
基于自适应白化的音乐节拍实时跟踪算法*

This paper proposed an adaptive-whitening-based real-time algorithm for music beat tracking, introduced adaptive whitening into onset detection phase of a music beat tracker. It preprocessed the STFT frames by adaptively normalizing the magnitude of each bin according to a present spectral peak table. This allowed each magnitude bin to achieve a similar dynamic range over time and improved onset detection performance for music with spectral roll-off effects and strongly varying dynamics. Experimental results on the MIREX 2006 dataset shows that the proposed adaptive-whitening-based beat tracker achieves superior performance and it can significantly improve the P-scores for music samples with strongly-varying dynamics.