Novel approach for musical instrument identification using neural network

This work aims to solve the problem of musical instrument identification in monophonic audio samples. The instruments chosen for this work were piano, flute, violin, drums and guitar. The audio data were sampled into frames of fixed size & then MFCC and few other TIMBRAL features were extracted from them. These features were used for training and testing the network. But instead of selecting one frame as an input, a different method was used to create a training and testing inputs for the classifier. Several experiments were conducted to obtain the best possible network using different training algorithms, learning rates and number of epochs. The results obtained from the experiments suggest that the best obtained network is an efficient classifier of the musical instruments under consideration.