DEEP LEARNING APPROACH FOR MUSIC GENERATION USING RNN

Automated music composition has become a significant area
of research in Artificial Intelligence. Traditional methods, such as
evolutionary algorithms, often struggle to maintain long-term structural
coherence in melodies. To address this, this paper proposes a Deep
Learning approach using Recurrent Neural Networks (RNN) for the
generation of high-quality music sequences. By leveraging the sequential
processing capabilities of RNNs, the system is trained to learn complex
musical patterns, note transitions, and rhythmic structures from diverse
datasets. Unlike heuristic-based fitness functions, the RNN model
automatically extracts features and predicts succeeding notes with high
accuracy. The results demonstrate that the proposed RNN-based model
can effectively compose melodies that exhibit both creativity and
structural consistency, providing a robust framework for hybrid-genre
music generation The research showed that each technique can promote
an 86% success rate, as the SVM demonstrated when tested on the same
dataset used in the research. Nevertheless, the current study does not say
that a high success rate can be achieved on all datasets.