Musical instrument classification and duet analysis employing music information retrieval techniques

The aim of this paper is to present solutions related to identifying musical data. These are discussed mainly on the basis of experiments carried out at the Multimedia Systems Department, Gdansk University of Technology, Gdansk, Poland. The topics presented in this paper include automatic recognition of musical instruments and separation of duet sounds. The classification process is shown as a three-layer process consisting of pitch extraction, parametrization, and pattern recognition. These three stages are discussed on the basis of experimental examples. Artificial neural networks (ANNs) are employed as a decision system and they are trained with a set of feature vectors (FVs) extracted from musical sounds recorded at the Multimedia Systems Department. The frequency envelope distribution (FED) algorithm is presented, which was introduced to musical duet separation. For the purpose of checking the efficiency of the FED algorithm, ANNs are also used. They are tested on FVs derived from musical sounds after the separation process is performed. The experimental results are shown and discussed.