This paper addresses the usefulness of the segmentation of musical sounds into transient/non-transient parts for the task of machine recognition of musical instruments. We put into light the discriminative power of the attack-transient segments on the basis of objective criteria, consistent with the well-known psychoacoustics findings. Moreover, we show that, paradoxically, it is not always optimal to consider such a segmentation of the audio in a machine recognition system given decision length constraints. Our evaluation exploits efficient automatic segmentation techniques, a wide variety of signal processing features as well as feature selection algorithms and Support Vector Machine classification. The sound database used is composed of real-world mono-instrument phrases.
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