Applying Recurrent Neural Networks with integrated Attention Mechanism and Transformer Model for Automated Music Generation

One of the key applications of artificial intelligence has come out in the form of automated music generation, that is a hybrid of creativity and computational models. The main purpose of this research is to develop and understand the blend of advanced Recurrent Neural Networks(RNNs) architecture with attention mechanisms to improve the limitations of ongoing RNNs in capitalizing long term dependencies. The main targets of the model are on relevant segments of input sequences,ensuring enhanced consistency and structural strength in the generated music industry. By comparison of enhanced RNN with a Transformer based model known for its exceptional capacity to model long-range dependencies through self-assessment. Additionally to filter rhythmic exactness and style, a beat-level segmentation method is implemented into this process. The structural composition of the generated outputs is examined using a Self-Similarity Matrix (SSM), which balances between reiteration and diversity.