Musical Onset Detection Based on Adaptive Linear Prediction

A new musical onset detection technique based on adaptive linear prediction theory is proposed in this work. We decompose a music signal into multiple sub-bands, and then apply a forward linear prediction error filter (LPEF) to model the narrow-band signal in each band, respectively. To enhance the modeling performance, the coefficients of the LPEF are updated with the least-mean-squares (LMS) algorithm. Under this framework, the onset detection problem can be formulated as the peak-error location problem. Peak selection algorithms are applied to prediction errors to locate the onset time. It is shown by experimental results that the proposed algorithm outperforms several well known existing methods for onset detection