Published Paper Details:

This paper introduces a pioneering method for music generation employing Variational Autoencoder (VAE) architecture, a powerful to Autoencoder of deep learning. Leveraging the VAE's capacity to learn latent representations of intricate data distributions, our approach encodes symbolic music representations into continuous latent spaces, enabling the generation of diverse and coherent musical sequences. Through training on a dataset of musical compositions, the VAE captures underlying structural nuances and stylistic elements, facilitating the generation of novel musical pieces by sampling latent vectors and decoding them into symbolic notation. We evaluate the efficacy of our methodology through quantitative metrics assessing diversity, coherence, and stylistic fidelity, alongside qualitative human evaluations of the generated music. Our findings illustrate that the VAE-based approach yields music compositions exhibiting both diversity and coherence, while maintaining fidelity to the stylistic attributes of the training data, suggesting the potential of VAEs as a compelling tool for creative music composition. This research contributes to the evolving landscape of deep learning in music generation, underscoring the promise of Variational Autoencoders in capturing and generating intricate musical structures.