Application of Multiacoustic Data in Feature Extraction of Anemometer

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.

A Novel Piano Arrangement Timbre Intelligent Recognition System Using Multilabel Classification Technology and KNN Algorithm

“…Kailash et al introduced the recognition principle of machine auditory system and applied the heuristic processing method to the sound signal, which can preliminarily understand the sound signal [ 4 ]. Chen and Guo put forward a timbre model based on piano pronunciation waveform, which uses the spectrum characteristics and amplitude envelope characteristics of timbre to construct piano timbre model [ 5 ]. Wu et al introduced a timbre model based on trapezoidal wave.…”