Academy of Music, Yuxi Normal University, Yuxi 653100, China.
Faculty of Data Science, City University of Macau, Macau 999078, China.
Comput Intell Neurosci. 2022 Jul 9;2022:2205936. doi: 10.1155/2022/2205936. eCollection 2022.
In this paper, melody and harmony are regarded as the task of machine learning, and a piano arranger timbre recognition system based on AI (Artificial Intelligence) is constructed by training a series of samples. The short-time Fourier transform spectrum analysis method is used to extract the piano timbre characteristic matrix, and the electronic synthesis of timbre recognition is improved by extracting the envelope function. Using the traditional multilabel classification method and KNN (K-nearest neighbor) algorithm, a combined algorithm of these two algorithms is proposed. The experimental results show that the detection rate increases from 61.3% to 70.2% after using the combined classification algorithm. The correct rate also increased from 40.3% to 48.9%, and the detection rate increased to 74.6% when the K value was set to 6. The experimental results show that, compared with the traditional classification algorithm, this algorithm has a certain improvement in recognition rate. Using this system to recognize the timbre of piano arrangement has a high recognition accuracy, which is worthy of further popularization and application.
本文将旋律和和声视为机器学习任务，通过训练一系列样本，构建了一个基于人工智能（AI）的钢琴编曲音色识别系统。采用短时傅里叶变换频谱分析方法提取钢琴音色特征矩阵，通过提取包络函数来改进音色识别的电子合成。利用传统的多标签分类方法和 KNN（K-最近邻）算法，提出了这两种算法的组合算法。实验结果表明，使用组合分类算法后，检测率从 61.3%提高到 70.2%，正确率也从 40.3%提高到 48.9%，当 K 值设置为 6 时，检测率提高到 74.6%。实验结果表明，与传统分类算法相比，该算法在识别率方面有一定的提高。使用该系统识别钢琴编曲的音色具有较高的识别精度，值得进一步推广和应用。
