Piano Pattern

Research on piano patterns focuses on understanding and modeling the fundamental building blocks of musical phrases, encompassing chords, scales, and progressions. Current efforts leverage machine learning, particularly neural networks like convolutional and recurrent architectures (including LSTMs and CRNNs), to identify, generate, and classify these patterns from both symbolic and audio representations of music. Large datasets of labeled piano patterns are being developed to facilitate the training and benchmarking of these models, enabling advancements in music information retrieval and automated music composition. This work contributes to a deeper understanding of musical structure and facilitates the development of novel music technologies.