LSTM-Based Music Generation Technologies

In deep learning, Long Short-Term Memory (LSTM) is a well-established and widely used approach for music generation. Nevertheless, creating musical compositions that match the quality of those created by human composers remains a formidable challenge. The intricate nature of musical components, including pitch, intensity, rhythm, notes, chords, and more, necessitates the extraction of these elements from extensive datasets, making the preliminary work arduous. To address this, we employed various tools to deconstruct the musical structure, conduct step-by-step learning, and then reconstruct it. This article primarily presents the techniques for dissecting musical components in the preliminary phase. Subsequently, it introduces the use of LSTM to build a deep learning network architecture, enabling the learning of musical features and temporal coherence. Finally, through in-depth analysis and comparative studies, this paper validates the efficacy of the proposed research methodology, demonstrating its ability to capture musical coherence and generate compositions with similar styles.