On the usefulness of differentiated transient/steady-state processing in machine recognition of musical instruments

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. The sound database used is composed of real-world mono-instrument phrases. Moreover, we show that, paradoxically, it is not always optimal to consider such a segmentation of the audio signal in a machine recognition system for a given decision window. 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.