Abstract: 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.

Key words: artificial intelligence, pattern recognition, neural network, deep learning, audio classification