Generative adversarial network for adaptive piano accompaniment

With the development of artificial intelligence, there has been a wave of innovation in the field of music creation and performance. In this paper, we propose a novel generative adversarial network architecture, which can generate adaptive piano accompaniment in real time. In the network, the generator generates piano accompaniment based on the input melody, and the discriminator evaluates the quality difference between the generated accompaniment and the professional accompaniment, and improves the quality of the accompaniment through the adversarial learning mechanism. The network design focuses on harmonizing the accompaniment with the main melody, introduces music theory knowledge constraints such as harmonic acoustic principles, and uses recurrent neural networks to capture the temporal characteristics of music, so that the rhythm and dynamics of the accompaniment change synchronously with the main melody. To validate the model, we collected piano repertoire of different styles and conducted extensive experiments. The results show that the generative adversarial network has good adaptability, can automatically adjust the accompaniment style and complexity according to the melody characteristics, and the generation speed is up to 5 seconds per song, and the adaptability is up to 90%, which is significantly better than the traditional method. User studies have shown that this network-generated accompaniment is audibly indistinguishable from that performed by a professional pianist, highlighting its potential for practical applications.