Generative Adversarial Network Based Music Generation

Music has been an integral part of human civilization personally and culturally. Historically, music has been generated using various instruments, or natural sounds like water drops, or unconventional musical instruments like metal or glass-wares. At present, technologies like Musical Instrument Digital Interface (MIDI) are used to generate music electronically. This research investigates the use of Generative Adversarial Networks (GANs) for beginner-friendly music production. This model uses Long Short-Term Memory (LSTM) generator and Patch GAN as discriminator for the GAN architecture. The generator consists of input layer, embedding layer, LSTM layer and generates output with a SoftMax function. Similarly, the discriminator consists of a convolution layer, the output of which is averaged by the global average pooling layer, and output is generated by the sigmoid function. The model is trained on Maestro MIDI dataset. We make the process understandable by delving into the implementation specifics and outlining the fundamental concepts of music. Our effective model highlights the potential of GANs in music composition by producing cohesive music. After training for 50 epochs, the model exhibited a remarkable precision of 91.82 percent. This project uses the combination of Artificial Intelligence (AI) with music theory to provide intriguing new opportunities in the field of music. The model can be beneficial for different industries like gaming, music, entertainment, education, etc.