CLSTM-DQN Fusion for AI-Driven Broken Chord Melodies

This research study introduces an innovative approach by employing LSTM networks to generate broken chord compositions, capturing nuanced harmonies and rhythms inherent in this fragmented structure. Trained on annotated sequences, the model adeptly retains the arpeggiated essence of broken chords. Leveraging LSTM networks, attention mechanisms, and meticulous architectural refinements, this method substantially elevates the quality and ingenuity of the compositions generated. The research significantly contributes to AI-driven music generation by offering robust composition support and personalized music recommendations.