Soochow University School of Music, Suzhou 215123, China.
J Environ Public Health. 2022 Jul 5;2022:6045597. doi: 10.1155/2022/6045597. eCollection 2022.
Piano note recognition is a process that converts music audio files into digital music files automatically, which is critical for piano assistant training and automatic recording of musical pieces. The Merle spectral coefficients, for example, have been used to implement the majority of the existing examples. The piano is one of the most popular forms of student education in today's world. Piano teachers should be aware of the implications. We can only truly adapt piano teaching to the educational purposes of higher education institutions if we implement a systematic, progressive, practical, and innovative philosophy of piano teaching. The Markov model is a statistical model that is widely used in speech signal processing. This thesis develops a set of mathematical models for piano speech recognition based on the Markov model, learns them systematically and scientifically, and achieves a better teaching effect. It is demonstrated that the Markov method detects the corresponding endpoints with an accuracy of 72.83 percent, which is 16.42 percent better than the a priori method. In terms of amplitude and phase, the Markov model shows a significant improvement. The findings of this study can be used to improve piano playing techniques taught to students in accordance with their favourite popular music, depending on the theme.
钢琴音符识别是一种将音乐音频文件自动转换为数字音乐文件的过程，这对于钢琴助手的训练和音乐作品的自动录制至关重要。例如，Merle 光谱系数已被用于实现大多数现有的示例。钢琴是当今世界学生教育最受欢迎的形式之一。钢琴教师应该意识到这一点。只有实施系统的、渐进的、实用的和创新的钢琴教学理念，我们才能真正将钢琴教学适应高等教育机构的教育目的。马尔可夫模型是一种广泛应用于语音信号处理的统计模型。本论文基于马尔可夫模型开发了一套钢琴语音识别的数学模型，对其进行了系统而科学的学习，并达到了更好的教学效果。实验结果表明，马尔可夫方法的端点检测准确率为 72.83%，比先验方法提高了 16.42%。在幅度和相位方面，马尔可夫模型也有显著的改进。根据主题，本研究的结果可用于改进针对学生喜欢的流行音乐的钢琴演奏技巧的教学。
