Deep Learning-Based Music Chord Family Identification

Research in the field of audio signal processing has developed considerably and music signal processing has not been an exception to this. Musicians from all over the globe have benefited tremendously with different technological advancements thereby leading music industry on to the next level. Music composers and DJs are always interested in the background music (BGM) of a song which is extremely critical in setting the mood. It is also very important for automatic music transcription and track composition for stage performers. Chords are one of the fundamental entities of BGM which are constituted with the aid of two or more musical notes. Identification of chords is thus a very important task which becomes challenging when the audio clips are short or not of studio quality. In this paper, a system is presented which can aid in distinguishing chords based on their type/family. We have experimented with two of the most fundamental type of chords major and minor at the outset and obtained a highest accuracy of 99.28% for more than 6000 very short clips of one-second duration with a deep learning-based approach.