基于深度学习的竹笛吹奏技巧自动分类

提出了一种针对竹笛技巧分类的数据集Breath和两个用于竹笛技巧分类的神经网络参考模型Breath1d和Breath2d，并针对此数据集的不同分类任务给出了最佳方法。将Breath数据集划分成子集，以多层感知机为性能评价基准方法，先用Breath1d和Breath2d模型对子集进行训练和预测，再用长短期记忆网络模型进行辅助测试，最后得出了最适合子任务的分类参考模型。对全数据集进行分类时，将Breath2d与Breath1d模型进行融合，并采用数据增强方法使全集分类准确率达到0.913。与传统音频分类任务相比，该工作扩展了音乐分类的研究领域，对民族音乐现代化发展有着良好的推动作用。

Automatic Classification of Bamboo Flute Playing Skills Based on Deep Learning

A dataset named Breath and two neural network reference models named Breath1d and Breath2d respectively are proposed for bamboo flute skill classification, and the optimal method is achieved for different classification tasks on this dataset. This paper divides the Breath dataset into subsets, and takes the multi-layer perceptron as the benchmark method of performance evaluation. First, the subsets are trained and predicted by the breath1d and breath2d models, and then the long short-term memory (LSTM) network model is used for auxiliary testing. Finally, the most suitable classification reference model for subtasks is obtained. When the whole dataset is classified, the breath2d and breath1d models are fused, and the data enhancement method is used. All of these make the classification accuracy of the whole dataset reach 91.3%. Compared with traditional audio classification tasks, this work expands the research field of music classification, and has a great effect on the modernization of national music.