  The task of efficient automatic music classification is of vital importance
and forms the basis for various advanced applications of AI in the musical
domain. Musical instrument recognition is the task of instrument identification
by virtue of its audio. This audio, also termed as the sound vibrations are
leveraged by the model to match with the instrument classes. In this paper, we
use an artificial neural network (ANN) model that was trained to perform
classification on twenty different classes of musical instruments. Here we use
use only the mel-frequency cepstral coefficients (MFCCs) of the audio data. Our
proposed model trains on the full London philharmonic orchestra dataset which
contains twenty classes of instruments belonging to the four families viz.
woodwinds, brass, percussion, and strings. Based on experimental results our
model achieves state-of-the-art accuracy on the same.
