Music Beat Generation through AI

Music Plays a significant role in our mental health, but composing a song is difficult for composers. While listening to music, Humans can quickly detect and move to beats. Similarly to Language, Music can be represented as a sequence of discrete symbols that form a hierarchical syntax, with notes being roughly like characters and motifs of notes like a word. Several Theoretical and algorithmic approaches have been proposed to account for this ability. Related to, but different from the issue of how we perceive a beat is a question of how we learn to generate and hold a beat. In this paper, we introduce a Music Beat Generator that will interpolate music rhythms and notes that can be used by music artists, Using Artificial intelligence and Machine Learning. In the context of this project, rhythms are one long measure sequence of notes and rest occurring on natural pulse subdivisions of a beat. Music generation using machine learning techniques has been a topic of interest for the past two decades. Music proves to be a different challenge compared to images, among three main dimensions: Firstly, music is temporal, with a hierarchical structure with dependencies across time. Secondly, music consists of interdependent instruments that unfold across time. Thirdly, music is grouped into chords, arpeggios, and melodies – hence each time -step may have multiple outputs.