  Traditionally, music was treated as an analogue signal and was generated
manually. In recent years, music is conspicuous to technology which can
generate a suite of music automatically without any human intervention. To
accomplish this task, we need to overcome some technical challenges which are
discussed descriptively in this paper. A brief introduction about music and its
components is provided in the paper along with the citation and analysis of
related work accomplished by different authors in this domain. Main objective
of this paper is to propose an algorithm which can be used to generate musical
notes using Recurrent Neural Networks (RNN), principally Long Short-Term Memory
(LSTM) networks. A model is designed to execute this algorithm where data is
represented with the help of musical instrument digital interface (MIDI) file
format for easier access and better understanding. Preprocessing of data before
feeding it into the model, revealing methods to read, process and prepare MIDI
files for input are also discussed. The model used in this paper is used to
learn the sequences of polyphonic musical notes over a single-layered LSTM
network. The model must have the potential to recall past details of a musical
sequence and its structure for better learning. Description of layered
architecture used in LSTM model and its intertwining connections to develop a
neural network is presented in this work. This paper imparts a peek view of
distributions of weights and biases in every layer of the model along with a
precise representation of losses and accuracy at each step and batches. When
the model was thoroughly analyzed, it produced stellar results in composing new
melodies.
