English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The accurate identification of an acoustic source is a fundamental problem in noise control. In real measurements, if a contribution of separate sources to the whole is identified properly, it is possible to apply appropriate modifications to the sound source that contributes most significantly to the whole signal. This paper presents a new approach to acoustic noise identification by using modern spectrum estimation, Grey Relational Analysis (GRA), and Back-Propagation neural networks. The modern spectrum estimation method was used to recognize the main noise source, and GRA was used to estimate the similarity among different curves of the power spectrum. Finally, the Artificial Neural Network (ANN) was used to do curve fitting, which give the results more wide applications. The ranking of the noise sources was determined on the basis of their individual contributions to the overall noise. The results of simulations confirmed the feasibility and validity of the method proposed in this paper, and it will play an important role in noise control, signal source identification, and other fields.

Research of on-line noise source identification based on the Grey neural network