Abstract
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.
Highlights
Dawei Chen and Xu GuoReceived 1 June 2021; Revised 27 June 2021; Accepted 17 July 2021; Published 24 July 2021
In the traditional instrument recognition methods, the recognition and cognition of instruments are generally realized through the angle analysis of the unique characteristics of instruments and in-depth auditory or visual capture [1]. e core content of instrument recognition is to evaluate the accuracy and efficiency of recognition, which is of great value to promote the intelligent development of a variety of acoustic data and instrument combination [2]
In the process of building the multiacoustic data analysis model, this study first selects three parameters related to the multiacoustic data and wind instrument features through the deep confidence network classification algorithm based on adaptive learning factor and proposes a multiacoustic data and instrument recognition management system based on dictionary learning and neighborhood regression [20]
Summary
Received 1 June 2021; Revised 27 June 2021; Accepted 17 July 2021; Published 24 July 2021. Is 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. E experiment evaluates the error of the network classification algorithm and describes the evaluation function of the deep belief network classification algorithm in the system. E traditional SNR evaluation method is used to improve the deficiency of evaluation function. Rough the deep belief network classification algorithm for self-learning, the instrument recognition method with strong applicability is established. The effectiveness of multiacoustic data in wind power instrument feature extraction is verified
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