Design of an Improved Model for Music Sequence Generation Using Conditional Variational Autoencoder and Conditional GAN

Whilst the task of creating music sequences through artificial intelligence has been met with a lot of attention, current approaches tend to stumble when it comes to capturing rich structures and subtle detail in musical compositions. Conventional models lack the capacity to build temporal dependencies as well as incorporate condition mechanisms (such as genre or mood), counteracting in less-ideal generation quality. In this work we address the above limitations and propose a hybrid model incorporating Dynamic Conditional Variational Autoencoder (CVAE) [15] and Generative Adversarial Network for music sequence generation from MIDI files. Our proposed approach utilizes a piano-roll representation and an event-based encoding for pre-processing, retaining structural as well as temporal complexities of music samples at the level of events. Meanwhile, the CVAE part (in a Seq2Seq architecture) is introduced to model conditioned latent distribution over music sequence attributes such as genre or composer identity in order contribute both generation and reconstruction of realistic musical samples. Furthermore, a Conditional GAN (cGAN) along with Wasserstein GAN with Gradient Penalty (WGAN-GP) is utilized for generating sequences of better quality by creating more realistic and conditionally plausible music samples. The results we show demonstrate that this hybrid CVAE-GAN framework can not only significantly improve the quality of generated music sequences (in terms like FAD) but also allow for a better listening experience, outperforming state-of-the-art evaluation methods. The work sets up a potential future in which AI helps musicians compose more nuanced music that is almost indistinguishable from the product of human creativity.