{"title":"A Frequency-weighting Digital Filter in Sound Level Meter based on Neural Computing Method","authors":"Haiyun Lin, Xinjie Shen, Gang Long, Haijun Lin","doi":"10.1142/s021947752450007x","DOIUrl":null,"url":null,"abstract":"Frequency weighting networks are a critical component of a sound level meter (SLM), and their error characteristics directly determine the performances of SLM. For reducing the high-frequency error of the [Formula: see text] frequency-weighting filters with the bilinear transformation method (BTM), a design method for [Formula: see text] frequency-weighting filters based on neural computing method (NCM) is proposed. A detailed algorithm for solving the filter coefficients is provided, and the amplitude-frequency characteristics of the [Formula: see text] frequency-weighting filters with BTM and NCM are compared in detail. The experimental results show that the amplitude-frequency characteristics of the [Formula: see text] frequency-weighting filters in SLM with NCM are significantly better than those of BTM. The filter meets the requirements of the first class SLM defined by IEC61672, which demonstrates the effectiveness of this proposed method.","PeriodicalId":55155,"journal":{"name":"Fluctuation and Noise Letters","volume":null,"pages":null},"PeriodicalIF":1.2000,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Fluctuation and Noise Letters","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1142/s021947752450007x","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
Frequency weighting networks are a critical component of a sound level meter (SLM), and their error characteristics directly determine the performances of SLM. For reducing the high-frequency error of the [Formula: see text] frequency-weighting filters with the bilinear transformation method (BTM), a design method for [Formula: see text] frequency-weighting filters based on neural computing method (NCM) is proposed. A detailed algorithm for solving the filter coefficients is provided, and the amplitude-frequency characteristics of the [Formula: see text] frequency-weighting filters with BTM and NCM are compared in detail. The experimental results show that the amplitude-frequency characteristics of the [Formula: see text] frequency-weighting filters in SLM with NCM are significantly better than those of BTM. The filter meets the requirements of the first class SLM defined by IEC61672, which demonstrates the effectiveness of this proposed method.
期刊介绍：
Fluctuation and Noise Letters (FNL) is unique. It is the only specialist journal for fluctuations and noise, and it covers that topic throughout the whole of science in a completely interdisciplinary way. High standards of refereeing and editorial judgment are guaranteed by the selection of Editors from among the leading scientists of the field.
FNL places equal emphasis on both fundamental and applied science and the name "Letters" is to indicate speed of publication, rather than a limitation on the lengths of papers. The journal uses on-line submission and provides for immediate on-line publication of accepted papers.
FNL is interested in interdisciplinary articles on random fluctuations, quite generally. For example: noise enhanced phenomena including stochastic resonance; 1/f noise; shot noise; fluctuation-dissipation; cardiovascular dynamics; ion channels; single molecules; neural systems; quantum fluctuations; quantum computation; classical and quantum information; statistical physics; degradation and aging phenomena; percolation systems; fluctuations in social systems; traffic; the stock market; environment and climate; etc.
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