Musical Onset Detection Using Constrained Linear Reconstruction

This letter presents a multi-frame extension of the well-known spectral flux method for unsupervised musical onset detection. Instead of comparing only the spectral content of two frames, the proposed method takes into account a wider temporal context to evaluate the dissimilarity between a given frame and its previous frames. More specifically, the dissimilarity is measured by using the previous frames to obtain a linear reconstruction of the given frame, and then calculating the rectified, l₂-norm reconstruction error. Evaluation on a dataset comprising 2,169 onset events of 12 instruments shows that this simple idea works fairly well. When a non-negativity constraint is imposed in the linear reconstruction, the proposed method can outperform the state-of-the-art unsupervised method SuperFlux by 2.9% in F-score. Moreover, the proposed method is particularly effective for instruments with soft onsets, such as violin, cello, and ney. The proposed method is efficient, easy to implement, and is applicable to scenarios of online onset detection.