The presented study is aimed to extract parameters from musical sounds that can be useful in the musical sound recognition process. For this purpose time-frequency transform analysis employing various filters is performed on musical sounds representing twelve instrument classes. Three groups of instruments are taken into account, namely: wind, string and percussive. Examples of wavelet analyses of various musical instrument sounds are presented. On this basis a number of parameters was extracted and statistically analyzed. Parameters that are correlated are removed from the feature vector. In this way a number of parameters in the feature vector can be diminished from dozens to a few most important ones. Then feature vectors were fed to the Artificial Neural Network inputs and classification experiments were performed. Furthermore originally developed Frequency Envelope Distribution method was applied to divide musical signal into harmonic and inharmonic content. Those signals were also parameterized and used in recognition experiments. Some experiment results are presented. The derived conclusions are also included in the paper.
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