Extracting note onsets from musical recordings

Automatic temporal segmentation of music signals into note onsets is central for a large number of audio applications. In this paper, we present a variation of a previously existing note onset detection method, based on the so-called spectral energy flux. The proposed algorithm has a lower computational cost and incorporates a more accurate estimation of the frequency content derivative, yielding better results for a wide range of music signals. The performance of the system was validated using a database of musical recordings containing 670 note onsets. This database was hand-labeled and cross validated by three annotators. Comparisons to previous work are also presented along with possible directions of future research.