Abstract
Music stretching resistance (MSR) is a fresh but important concept in audio signal processing, which characterizes the ability of a music piece to be stretched in time (compressed or elongated) without objectionable perceptual artifacts. It has the potential to be highly demanded in various multimedia applications like music resizing, audio editing and multimedia integration, but there is almost no prior knowledge about this property of music in literature. In this letter, the task of MSR is formulated for the first time, and an MSR classification method that employs metric learning on audio features and genres is also proposed. It attempts to automate what human acceptable time-stretching rate range of music should be. The proposed method outperforms the reference classification methods in accuracy in the comparative experiments.
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