Drum Instrument Classification Using Machine Learning

Abstract

Music is a way to express our creativity. As an art form, music can go beyond the limits of human imagination. When one hears a piece of music or sounds, human brain releases chemical dopamine. Hearing sounds again and again repetitively allows us to remember characteristics and nature of sound in a very efficient way. This is known as auditory learning and is believed to occur in our day to day life which helps us in identifying, memorizing and classifying various sounds. It allows, for example, immediate recognition of sounds or voices which become familiar through experience. The exact same principle can be implemented using Machine Learning. Music and Mathematics are strongly correlated with each other, whether it be the waveform or the sequence in which the melody is being played. In this paper, a Drum Instrument Classification Model is implemented using Machine Learning. The data is self prepared by recording samples and by using a Drum Simulator. The initial dataset contains only audio files in .wav format. The pivotal task is to perform Feature Extraction from the audio files and using them to train the Machine Learning model. Finally, a model is created which is capable of classifying various drum instruments when provided with an audio input.