Improved AI-Generated Multitrack Music with Variational Autoencoders (VAE) for Harmonious Balance Compared to Recurrent Neural Network for Coherence

This paper, therefore, proposes the application of the VAEs in enhancing the coherence and the alignment of the multiple track music AI-sync’d above. In our case, we want to take advantage of the feature of VAEs in modelling the complex data distribution. This will help create models that are indeed synthetic in the interconnectivity of the musical aspects. As for the evaluation, our method is designed for the assessment of the methodology of music generation, thus we compare the suggested approach with the baselines, namely, Variational Auto Encoders and Recurrent Neural Networks. The studies that have been discussed above help in a number of ways to AI music generation from the given VAE instrument through an understanding of a capacity to create aesthetically appealing and intricate music. The two high-level algorithmic methodologies were employed on 20 samples but from LPD dataset hence arriving at a total of 40 samples. The first priority used was CAO with reference to the structural integration and suitability of individual musical tracks. The findings that have been established in this paper reveal that a new model which has been developed known as VAE is a better model of synthesizing logical multitrack music compared to the RNN.