Fourier analysis is used as a framework in which to obtain spectral estimates for nonstationary discrete time signals. Analysis of the auto-power spectrum and the cross-power spectrum makes it possible to distinguish between false alarms and real risk situations and, in the latter case, to determine the propagation of any possible damage in its earliest phases. This analysis then allows corrective actions to be taken, such as parts replacement, in order to limit damage and contamination of component parts in fatigue tests and thus shorten machine stoppage times.
Within a Music Information Retrieval perspective, the goal of the study presented here is to investigate the impact on sound features of the musician's affective intention, namely when trying to intentionally convey emotional contents via expressiveness. A preliminary experiment has been performed involving 10 tuba players. The recordings have been analysed by extracting a variety of features, which have been subsequently evaluated by combining both classic and machine learning statistical techniques. Results are reported and discussed.
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