Exploring Hybrid GRU-LSTM Networks for Enhanced Music Generation

The use of deep learning for creating music has been receiving a lot of interest nowadays due to its capacity for innovation and originality. This paper investigates how well hybrid networks that combine Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM) units perform music-generating tasks. Improving the model's ability to identify long-term relationships and maintain context while generating musical sequences is the aim of the proposed hybrid architecture. It accomplishes this by fusing the benefits of LSTM and GRU units. Comprehensive experiments are conducted on multiple music datasets to evaluate the performance of the hybrid GRU-LSTM networks in generating musical compositions. The quality of created music sequences is evaluated using performance measures like overall musicality, harmonic consistency, and melody coherence. Expert musicians also conduct qualitative assessments to offer insights into the creative and artistic elements of the developed compositions. When compared to current LSTM-based models, the results show how well the hybrid GRU-LSTM networks perform in generating high-quality music sequences with better coherence, consistency, and inventiveness. Furthermore, the study investigates the effects of various coaching strategies and design features on the performance of hybrid networks. All things considered, by exploring novel architectures and strategies for applying deep learning techniques to enhance the uniqueness and quality of generated music, this research enhances the field of music creation. The findings shed light on how hybrid GRU-LSTM networks might encourage innovation in the sector and raise the bar for music production skills.