Classification of transient time-varying signals using DFT and wavelet packet based methods

The classification of transient time-varying signals is important for industrial, biomedical and military applications. The attack phase of piano sounds is used as an example for transient, time-varying signals in a real data application. Discrete Fourier transform and time-invariant wavelet packet based algorithms are used alternatively for feature extraction. The training set is used for determining an appropriate feature selection. A classifier checks whether the generated features are sufficient in order to identify the correct piano. Classification results are presented and discussed.