Deep learning for acoustic signal processing for industrial noise

Abstract

The aim of this paper is to provide an overview of the existing practices used for acoustic signal processing of noise of machines in various industries. There has been a surge in deep learning based methods for acoustic detection and classification of machinery fault diagnosis. This paper reviews the deep learning models, including the convolutional neural networks, the recurrent neural networks, the spiking neural networks, among the other variants of neural network models specific to industrial noise. Important applications such as sound detection, localization, directivity, source separation are discussed, to aid condition monitoring.

Application of Multiacoustic Data in Feature Extraction of Anemometer

“…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 recent years, many mathematicians mainly focus on musical instrument timbre optimization, musical instrument structure improvement, and so on, rarely through acoustic data to study the intelligent instrument recognition system [3]. In China, the research on article feature recognition has a history of decades, involving a lot of content [4].…”