Hybrid Feature Based Classifier Performance Evaluation of Monophonic and Polyphonic Indian Classical Instruments Recognition

Instrument recognition in computer music is an important research area that deals with sound modelling. Musical sounds comprises of five prominent constituents which are Pitch, timber, loudness, duration, and spatialization. The tonal sound is function of all these components playing critical role in deciding quality. The first four parameters can be modified, but timbre remains a challenge [6]. Then, inevitably, timbre became the focus of this piece. It is a sound quality that distinguishes one musical instrument from another, regardless of pitch or volume, and it is critical. Monophonic and polyphonic recordings of musical instruments can be identified using this method. To evaluate the proposed approach, three Indian instruments were experimented to generate training data set. Flutes, harmoniums, and sitars are among the instruments used. Indian musical instruments classify sounds using statistical and spectral parameters. The hybrid features from different domains extracting important characteristics from musical sounds are extracted. An Indian Musical Instrument SVM and GMM classifier demonstrate their ability to classify accurately. Using monophonic sounds, SVM and Polyphonic produce an average accuracy of 89.88% and 91.10%, respectively. According to the results of the experiments, GMM outperforms SVM in monophonic recordings by a factor of 96.33 and polyphonic recordings by a factor of 93.33.