We investigate a novel gradient-based musical feature ex-tracted using a scale-invariant feature transform. This fea-ture enables dynamic information in music data to be effec-tively captured time-independently and frequency-independently. It will be useful for various music applica-tions such as genre classification, music mood classification, and cover song identification. In this paper, we evaluate the performance of our feature in genre classification experi-ments using the data set for the ISMIR2004 contest. The performance of a support-vector-machine-based method using our feature was competitive with the contest even though we used only one fifth of the data. Moreover, the experimental results confirm that our feature is relatively robust to pitch shifts and temporal changes.