Educational Evaluation of Piano Performance by the Deep Learning Neural Network Model

Abstract

In recent years, the piano education industry has occupied a huge market. However, the automatic evaluation function of piano performance has shortcomings in existing piano education products. Deep learning (DL) algorithm and the recurrent neural network (RNN) structure can help in automatics evaluation function of the piano performance. This paper proposes a Musical Instrument Digital Interface (MIDI) piano evaluation scheme based on the RNN structure and the Spark computing engine using the Deeplearning4J DL framework. The Deeplearning4J framework can run on the Java Virtual Machine; therefore, the entire system does not require cross-platform development. The Spark distributed computing engine realizes parallelization in music data preprocessing, feature extraction, and model training. Combined with the training user interface (UI) provided by the Deeplearning4J, it can improve developmental efficiency. Additionally, the RNN parameters are analyzed. The results demonstrate that the error value of the three-layer RNN structure is smaller than other closest rivals’ techniques. In particular, few piano training institutions and MIDI website datasets are used as the basis, and the experimental samples are collected. The neural network is trained, and the performance of the evaluation model is tested. The results show that the evaluation outcomes of the designed performance evaluation model for the piano are fundamentally consistent with the real levels of the players with assured feasibility; after 3k times of the training periods, the error of the RNN model is close to 0.01 and the network converges.