Deep Learning-Based Automatic Piano Note Recognition and Performance Generation System

This study proposes a deep learning-based automatic piano note recognition and performance generation system, which aims to enhance the accuracy and efficiency of piano music transcription and synthesis. Traditional methods for piano note recognition often rely on heuristic algorithms and handcrafted features, which struggle with complex polyphonic music and varying acoustic conditions. To address these limitations, we introduce an end-to-end deep learning framework that integrates convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to extract temporal and spectral features from piano audio recordings. The system is further enhanced with an attention mechanism to improve the differentiation of overlapping notes. A generative model is incorporated to synthesize expressive piano performances based on the recognized notes, ensuring a natural and human-like playing style. The proposed model is trained on large-scale piano performance datasets to enhance generalization across different playing styles and recording conditions. Furthermore, a reinforcement learning-based optimization strategy is introduced to refine the model’s performance in real-time applications. To improve robustness, the system integrates data augmentation techniques and adversarial training to mitigate errors caused by noise and variations in recording environments. Experimental results demonstrate that the proposed system achieves superior note recognition accuracy and generates high-quality piano performances compared to traditional approaches. These findings highlight the potential of deep learning in advancing automatic music transcription and synthesis technologies, paving the way for more interactive and intelligent music applications, such as real-time accompaniment systems, automatic music composition, and digital sheet music generation. This work contributes to bridging the gap between artificial intelligence and musical creativity, offering novel possibilities for both professional musicians and music enthusiasts.