本文選題：多通道噪聲測量　切入點：自卷積檢測　出處：《湖南大學》2015年博士論文

【摘要】：噪聲是一種人們不希望聽到的聲音,對人類的生活和健康產(chǎn)生巨大的影響,對動物、儀器設(shè)備和建筑物等各方面也產(chǎn)生一定的影響。噪聲污染同水污染、大氣污染和廢物污染一起被看成是世界范圍內(nèi)的四個主要環(huán)境問題。隨著社會經(jīng)濟的發(fā)展,噪聲污染問題越來越突出,帶來了一系列的不良效應,影響社會的和諧穩(wěn)定發(fā)展。因此,開展噪聲測量與分析技術(shù)的研究具有非常重要的意義�？陀^而全面的噪聲測量與分析是認識、判斷和處理各種噪聲問題的重要手段。隨著科技的發(fā)展以及計算機技術(shù)水平的提高,傳統(tǒng)噪聲測量儀器的功能和性能等發(fā)生了很大的變化,正向數(shù)字化、智能化、網(wǎng)絡(luò)化和虛擬化的方向發(fā)展。但是國內(nèi)外高性能噪聲測量儀器和系統(tǒng)價格比較昂貴,而一般的噪聲測量儀器和系統(tǒng)存在各種各樣的不足。本文采用理論推導、仿真分析和實驗驗證相結(jié)合的方法,擬設(shè)計和開發(fā)多通道噪聲測量與分析系統(tǒng),并且針對多通道噪聲測量與分析相關(guān)的關(guān)鍵理論開展深入的研究。主要有以下幾個方面:(1)在多通道噪聲測量過程中,由于各種因素的影響,尤其是背景噪聲的影響,每個通道測量得到的信號是不能直接被使用和處理的,否則會影響測量和評價的結(jié)果,因此針對噪聲測量的預處理問題,研究周期信號的自卷積去噪理論,提出自卷積檢測法,擬用于去除噪聲測量中的背景噪聲,提高被處理信號的信噪比。對各種不同頻率成分的信號進行仿真實驗。(2)在多通道噪聲測量過程中,由于聲音無處不在,所以傳聲器接收到的信號一般是多個噪聲源信號的混合,而如果要測量單個噪聲源信號,則可以先對混合多噪聲源進行分離。針對混響環(huán)境中多個噪聲源同時存在時的聲源分離和單個噪聲源實際輻射噪聲的測量問題,研究基于時間反轉(zhuǎn)技術(shù)(Time reversal technique,TRT)的聲源分離和測量方法,并在MATLAB環(huán)境中對混響室內(nèi)的二維和三維聲場進行建模仿真,討論不同的聲場參數(shù),如聲源位置、聲源類型、麥克風陣列布局、混響和通道噪聲對算法性能的影響。(3)在多通道噪聲測量與分析中,有些應用場合需要對每個通道測量的信號進行頻譜分析,分析噪聲的頻率成分或頻譜特征。由于快速傅里葉變換(Fast Fourier transform,FFT)算法本身的缺陷,對頻譜特征的提取需要進行離散頻譜校正,如估計質(zhì)心頻率。針對傳統(tǒng)的離散頻譜校正方法的不足,研究基于譜質(zhì)心(Spectral centroid,SC)的聲信號頻譜校正方法。討論SC應用于單頻信號和多頻信號的頻譜校正原理,尤其針對常見的聲信號估計寬頻和倍頻程的SC及質(zhì)心頻率,并且討論在背景噪聲下SC的變化。(4)由于在多通道噪聲測量與分析系統(tǒng)中,每個通道測量一路噪聲信號,且每個通道的數(shù)據(jù)是獨立處理和分析的,所以研究單通道噪聲測量數(shù)據(jù)的后處理方法仍然非常重要。針對單通道噪聲測量數(shù)據(jù)的信息融合和擬合問題,提出基于信息量和最小條件熵參數(shù)估計兩種信息融合方法以及基于互信息的數(shù)據(jù)擬合辨識方法。對于信息量融合方法,首先利用最大熵方法(Maximum entropy method,MEM)估計測量樣本的概率分布,再根據(jù)每個樣本的自信息量與樣本總體信息熵的比值對樣本數(shù)據(jù)進行融合;對于最小條件熵參數(shù)估計融合方法,根據(jù)觀測樣本的條件概率密度函數(shù)構(gòu)建觀測總體條件信息熵,最小化該條件信息熵,求解無約束極值問題即可得到最優(yōu)結(jié)果;對于互信息擬合辨識方法,將數(shù)據(jù)擬合的過程看作是一個通信過程,根據(jù)信息通信的信道模型構(gòu)建數(shù)據(jù)擬合模型,再根據(jù)擬合數(shù)據(jù)、擬合曲線和擬合誤差三者的信息熵求解擬合模型的互信息,選取互信息最大的曲線作為擬合曲線。(5)結(jié)合多通道噪聲測量與分析系統(tǒng)的需求和性能指標,以計算機為信息處理核心,結(jié)合虛擬儀器、數(shù)據(jù)庫、高速數(shù)據(jù)采集卡和數(shù)字信號處理等多種應用技術(shù)設(shè)計開發(fā)一款多通道噪聲測量與分析系統(tǒng),擬達到通道多、功能強、精度高、速度快、價格低等性能指標,詳細給出每個軟件模塊的設(shè)計原理和程序。
[Abstract]:Noise is a kind of unwanted sound, have a tremendous impact on human life and health of the animal, the equipment and buildings also have a certain impact. Noise pollution and water pollution, air pollution and waste pollution is regarded as the world within the scope of the four major environmental problems. With the development of social economy, the problem of noise pollution is becoming more and more prominent, brought a series of negative effects, affect social harmony and stability and development. Therefore, it is very important to study the development of noise measurement and analysis technology. Noise measurement and analysis of objective and comprehensive understanding, an important means to judge and deal with various noise problems. With the development of technology and improve the level of computer technology, has undergone great changes, the traditional noise measuring instrument function and performance are digital, intelligent, network The virtual and the direction of development. But the domestic and foreign high performance noise measuring instruments and systems are expensive, lack of noise measuring instruments and systems in general there are various. This paper uses the method of theoretical derivation, simulation analysis and experimental validation of the combination, to the design and development of multi channel noise measurement and analysis system, and for deep research of the key theory and related analysis of multi channel noise measurement. Mainly in the following aspects: (1) in multi channel noise in the measurement process, due to various factors, especially the influence of background noise, signal of each channel measurement is not to be used directly, otherwise it will affect the measurement and the results of the evaluation, so the pretreatment problem of noise measurement, self convolution denoising theory of periodic signal, the self convolution method, to be used for removing noise measurement In the background noise, improve signal processing by SNR. Signals of different frequencies is simulated. (2) in multi channel noise in the measurement process, the sound is everywhere, so signal received by the microphones are generally mixed multiple noise source signals, and if you want to measure a single noise source signal you can first, separating the mixed noise sources. To solve the problem of measuring the sound source separation and single noise source of multiple noise sources in reverberant environments exist at the same time the actual radiation noise, the research based on time reversal technique (Time reversal technique, TRT) of the sound source separation and measurement method, and two-dimensional in MATLAB environment the reverberation chamber and the three-dimensional acoustic modeling and simulation, discuss the acoustic parameters, such as the position of the sound source, the sound source type, microphone array layout, and the effects of reverberation channel noise on the performance of the algorithm in (3). Measurement and analysis of noise in the channel, some applications need to analysis the signal spectrum of each channel measurement and analysis of noise frequency components or spectrum characteristics. Because of the fast Fourier transform (Fast Fourier transform FFT) algorithm to extract the defect itself, the need for spectrum correction of discrete spectrum estimation, such as centroid frequency for. Lack of correction method for discrete spectrum of the traditional research based on spectral centroid (Spectral centroid, SC) of the sound signal spectrum correction method. Discuss the correction principle of spectrum of SC application in single frequency and multi frequency signal, especially for SC and centroid frequency broadband and octave estimation of acoustic signals in common, and discuss the changes in the background the noise of SC. (4) due to the multi channel noise measurement and analysis system, each channel measuring a noise signal, and each channel is independent of the data processing and analysis So, study on single channel noise measurement data postprocessing method is still very important. Based on the information fusion of single channel noise measurement and data fitting problem, based on the two kinds of information fusion method of information quantity and the minimum conditional entropy of parameter estimation and identification data fitting method based on mutual information. The information fusion method based on the maximum. Entropy method (Maximum entropy method, MEM) to estimate the probability distribution of the measured samples, then according to the ratio of each sample from the amount of information and the sample information entropy fusion of sample data; the small conditional entropy fusion method for parameter estimation, according to the observation samples of the conditional probability density function to construct general observation conditional information entropy, minimizing the conditional information entropy, solve the unconstrained extremum problem can get best results; mutual information for fitting the identification method, the process of data fitting at As a communication process, according to the construction of data fitting model of channel model of information communication, according to the fitting data, mutual information entropy fitting model fitting curve and the fitting error of the three, select the maximum mutual information as curve fitting curve. (5) according to the requirements and performance index of multi channel noise measurement and analysis the system, based on computer information processing core, combined with virtual instrument, database, the development of a multi channel noise measurement and analysis system of high speed data acquisition card and a variety of applications such as digital signal processing technology to design, to multi-channel, strong function, high precision, fast speed, low price performance index, design principle and procedure details are given of each software module.

【學位授予單位】：湖南大學
【學位級別】：博士
【學位授予年份】：2015
【分類號】：TB53

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