Deep Neural Network for Musical Instrument Recognition using MFCCs

TL;DR

This paper presents a neural network model that classifies twenty musical instrument classes using MFCC features, achieving state-of-the-art accuracy on a comprehensive orchestra dataset.

Contribution

The study introduces a neural network approach utilizing MFCCs for instrument recognition, demonstrating superior accuracy on a large, diverse dataset.

Findings

Achieved state-of-the-art accuracy on the London Philharmonic Orchestra dataset.

Successfully classified twenty instrument classes across four families.

Validated effectiveness of MFCC features with neural networks for music classification.

Abstract

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.