A Deep Hybrid Framework for Symbolic Music Generation Blending Sequential and Generative Models

Music is import and gives soothing to the human. There are different varieties music is present. The new music can be generated using different pitch and notes. Blending of Deep Learning (DL)-sequential and generative models such as LSTM, GRU, Transformer, Generative Adversarial Networks (GAN), Variational Autoencoder (VAE) and Reinforcement Learning (RL) is used as the hybrid frame work for generating the new music. The system is trained and evaluated using the midi_songs and maestro dataset. The model first captures temporal dependencies using LSTM and GRU layers, enhances long-range context with Transformer, compresses features using VAE, and refines output quality through adversarial training using GAN. Reinforcement Learning is applied to finetune the generation based on musicality rewards. The paper concentrates on the cosine similarity to evaluate the music. The hybrid model is able to get the accuracy of 8 2. 5%. Experimental results demonstrate improved coherence, diversity, and musical structure in generated compositions. The proposed architecture offers a significant advancement in deep generative music modeling.