The acoustic characteristics of wind instruments are a major feature in the field of vocal music. This paper studies the application effect of wind power instrument feature extraction based on multiacoustic data. Combined with the acoustic data training model, the classification algorithm based on deep trust network is used to process multiple acoustic data. Using multiple acoustic data for feature extraction, the recognition and matching between multiple acoustic data and wind measuring instrument are realized. The experiment not only evaluates the error of the network classification algorithm but also describes the evaluation function of the deep belief network classification algorithm in the system. The traditional SNR evaluation method is used to improve the deficiency of evaluation function. Through the deep belief network classification algorithm for self-learning, the instrument recognition method with strong applicability is established. Finally, the effectiveness of multiacoustic data in wind power instrument feature extraction is verified.

中文翻译：

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多声学数据在风速计特征提取中的应用

管乐器的声学特性是声乐领域的一大特色。本文研究了基于多声学数据的风电仪器特征提取的应用效果。结合声学数据训练模型，采用基于深度信任网络的分类算法对多个声学数据进行处理。利用多个声学数据进行特征提取，实现了多个声学数据与测风仪的识别匹配。实验不仅评估了网络分类算法的误差，还描述了系统中深度置信网络分类算法的评估函数。采用传统的信噪比评价方法来改善评价函数的不足。通过深度置信网络分类算法进行自学习，建立了适用性强的仪器识别方法。最后，验证了多声学数据在风电仪器特征提取中的有效性。