本文選題：有源噪聲控制 + LMS自適應(yīng)算法　； 參考：《內(nèi)蒙古工業(yè)大學(xué)》2013年碩士論文

【摘要】：被世界公認(rèn)為第三大公害的是噪聲污染，噪聲污染是現(xiàn)在十分關(guān)注的環(huán)境問題，若長期在低頻噪聲環(huán)境下工作、學(xué)習(xí)或生活，將對人的身體健康造成嚴(yán)重危害。在軍事領(lǐng)域，某些技術(shù)兵器的作戰(zhàn)性能也和噪聲問題息息相關(guān)。有源噪聲控制技術(shù)又被稱為主動噪聲控制（Active Noise Control，ANC）技術(shù)，它是以聲波的干涉現(xiàn)象為原理以實(shí)現(xiàn)降噪為目的的一種方法，近幾年來由于人們生活質(zhì)量的提高所以降噪問題又成為了研究熱點(diǎn)。 在有源噪聲控制中，我們想要的輸出已經(jīng)有了明確的要求，即是要和噪聲頻率相同，幅值相同，相位相差180°，而噪聲源是未知的，所以我們這里就要讓濾波器的參數(shù)根據(jù)前一時刻的值，自動調(diào)節(jié)參數(shù)來適應(yīng)噪聲源的變化產(chǎn)生我們想要的輸出，，也就是我們的自適應(yīng)算法。由于最小均方(LMS)自適應(yīng)算法比較簡單、易于實(shí)現(xiàn)，本文搭建實(shí)現(xiàn)了一個基于LMS算法的單通道有源噪聲控制系統(tǒng)及一個和上位機(jī)聯(lián)合控制的單通道有源噪聲控制系統(tǒng)。一般情況下，某一局部空間的噪聲可以認(rèn)為是若干復(fù)頻聲源信號從不同的傳播方向經(jīng)過不同的傳遞通道在此空間范圍內(nèi)的合成結(jié)果。這些噪聲源信號通常是具有一定帶寬的強(qiáng)相關(guān)信號，即它們具有相同的頻率成分。在這樣復(fù)雜的噪聲信號條件下，目前采用LMS算法的雙通道控制系統(tǒng)降噪效果甚微，本文又設(shè)計搭建了一個基于信號盲分離算法的雙通道控制系統(tǒng)。 本文選用的是TMS320VC5509ADSP數(shù)字信號處理器，該款芯片在語音識別及語音合成等方面所用到的數(shù)字信號處理算法是十分有效的。實(shí)驗結(jié)果表明，基于LMS算法的單通道降噪系統(tǒng)效果最好時可以取得1dB(A)左右的降噪效果，和上位機(jī)聯(lián)合控制的單通道有源噪聲控制系統(tǒng)可以取得13~16dB(A)的降噪效果，基于信號盲分離算法的雙通道降噪系統(tǒng)可以取得8~10dB(A)左右的降噪效果，本文最后對這幾種系統(tǒng)做了總結(jié)和分析提出了改進(jìn)的辦法。
[Abstract]:Noise pollution is recognized as the third major public hazard in the world. Noise pollution is a serious environmental problem. If we work, study or live in low-frequency noise environment for a long time, it will cause serious harm to human health. In the military field, the combat performance of some technical weapons is also closely related to the noise problem. The active noise control technology is also called Active noise Control (ANC) technology. It is a method based on the interference phenomenon of sound wave to achieve noise reduction. In recent years, due to the improvement of people's quality of life, noise reduction has become a research hotspot. In the active noise control, the output we want has a clear requirement, that is, the noise frequency is the same, the amplitude is the same, the phase difference is 180 擄, and the noise source is unknown. So we have to let the parameters of the filter automatically adjust the parameters according to the value of the previous time to adapt to the change of noise source to produce the output we want, that is, our adaptive algorithm. Because the least mean Square (LMS) adaptive algorithm is simple and easy to implement, a single channel active noise control system based on LMS algorithm and a single channel active noise control system based on LMS algorithm are built and implemented. In general, the noise in a local space can be considered as the composite result of a number of complex frequency sound source signals passing through different transfer channels in this space from different propagation directions. These noise source signals are usually strong correlated signals with certain bandwidth, that is, they have the same frequency components. Under such complicated noise signal condition, the noise reduction effect of two-channel control system based on LMS algorithm is very little. In this paper, a dual-channel control system based on blind signal separation algorithm is designed and built. In this paper, TMS320VC5509ADSP digital signal processor is selected. The digital signal processing algorithm used in speech recognition and speech synthesis is very effective. The experimental results show that the single channel noise reduction system based on LMS algorithm can achieve a noise reduction effect of about 1 dB (A) when the effect is the best, and a single channel active noise control system controlled by a host computer can achieve a noise reduction effect of 13 ~ 16 dB (A). The two-channel de-noising system based on blind signal separation algorithm can achieve a noise reduction effect of about 8 ~ 10dB (A). In the end, this paper summarizes and analyzes these systems and puts forward some improved methods.
【學(xué)位授予單位】：內(nèi)蒙古工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TB535

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 孫利峰;陳克安;謝達(dá);;多通道有源控制算法硬件實(shí)現(xiàn)及影響因素研究[J];電聲技術(shù);2007年02期

2 王登峰,劉學(xué)廣,劉宗巍,梁杰;基于發(fā)動機(jī)轉(zhuǎn)速的車內(nèi)有源消聲控制策略和自適應(yīng)算法[J];公路交通科技;2004年10期

3 姜泉璐;汪立新;呂永佳;吳玉彬;葉軍;;基于LMS自適應(yīng)濾波器的設(shè)計[J];電子設(shè)計工程;2011年14期

4 高建輝;;LMS自適應(yīng)濾波器的設(shè)計理論研究[J];信息技術(shù);2011年08期

5 王亞林;;LMS自適應(yīng)濾波器的算法原理及其DSP仿真實(shí)現(xiàn)[J];科技信息;2009年24期

6 劉義春;;縱波干涉的規(guī)律及其應(yīng)用[J];物理通報;2000年09期

7 曹斌芳;何怡剛;胡惟文;傅祖清;;一種改進(jìn)型的FIR數(shù)字濾波器設(shè)計[J];現(xiàn)代電子技術(shù);2006年04期

8 馬俊;段新文;趙建飛;;自適應(yīng)濾波器LMS算法的DSP實(shí)現(xiàn)[J];現(xiàn)代電子技術(shù);2008年13期

9 孫旭,陳端石;基于無限脈沖響應(yīng)濾波器的自適應(yīng)濾波E型有源噪聲控制算法[J];聲學(xué)學(xué)報;2003年02期

10 陳克安;陳國躍;李雙;潘浩然;;分布式位移傳感下的有源聲學(xué)結(jié)構(gòu)誤差傳感策略[J];聲學(xué)學(xué)報(中文版);2007年01期

本文編號：2066534