Modeling Method for Classification of Piano Music Style based on Big Data Mining and Machine Learning

With the progress of music digitalization, various styles of music have been produced, and effective classification of music has become an important research direction. In this research, a model for piano-music style classification was constructed based on big data mining and machine learning algorithms. The input music signal was dealt with using framing, signal enhancement, and windowing. The Meldor Frequency Coefficient (MFC) and emotional features in the signal were extracted and fused to obtain combined features. The extracted feature vectors were input into a Deep Belief Network (DBN) for training and then a hidden Markov model (HMM) for classification and recognition. However, it was found that during the HMM training process, the algorithm produces large differences in the randomly selected initial matrix parameters, which cause the results to be trapped at a local optimum and affect the accuracy of model classification and recognition. To optimize the parameters, a genetic algorithm was used to optimize the classification model. The average Relative Percent Difference (PRD) was 2.402, the run time was 2.117 s, and the accuracy was 97.074%, which means the model can efficiently and accurately classify piano music styles. © 2024 The Institute of Electronics and Information Engineers.