本文選題：有源噪聲控制 + 盲信號分離��； 參考：《內蒙古工業(yè)大學》2013年碩士論文

【摘要】：隨著現(xiàn)代工業(yè)的發(fā)展，噪聲污染越來越嚴重，傳統(tǒng)的噪聲控制方法對于中高頻噪聲控制效果較好，對低頻控制效果不明顯，而有源噪聲控制方法的提出為解決這一難題帶來了希望，并且由于其具有體積小、重量輕、易于控制等優(yōu)點，，使得很多專家和學者投入大量精力和時間致力于有源噪聲控制的研究。 現(xiàn)在有源噪聲控制技術中基本上都是根據線性自適應濾波理論通過不同的自適應控制算法來實現(xiàn)降噪的。但這類算法在收斂速度和穩(wěn)態(tài)失調上具有矛盾性，增大步長可以加快收斂速度，同時也會增大穩(wěn)態(tài)失調。并且通常這類自適應算法對于單通道可以取得較好的降噪量，但因多通道時計算量大、收斂速度慢，加上布放次級聲源的數目和位置難以準確給出，使得多通道降噪難以實現(xiàn)取得明顯效果。 針對現(xiàn)有控制方法在多通道降噪方面的不足，本文提出了將盲信號分離技術引入到有源噪聲控制中，并以兩通道降噪模型為例，著重介紹了兩噪聲源信號相關情況下盲信號的解析分離方法。通過仿真實驗證明了該降噪模型下實現(xiàn)相關信號分離的可行性和準確性。盲分離完成后得到的兩個信號就是兩噪聲信號，針對該信號做傅里葉變換，然后根據頻譜圖所示得到噪聲信號的主要頻率成分，利用這些主要頻率成分信息來完成次級聲源信號的構造。 這種基于盲信號分離的有源噪聲控制方法與傳統(tǒng)降噪方法的不同之處在于擯棄了傳統(tǒng)的自適應控制方法，采用盲信號分離技術實現(xiàn)混合聲源信號的分離。對于多通道降噪而言正是因為采用了盲分離技術，可以獲得各個噪聲信號的確切信息，從而使我們可以將多通道中的每個通道看做一個單通道去控制，能夠有針對性的來安排次級聲源的數目和位置，與傳統(tǒng)降噪方法相比這也是本文的創(chuàng)新點之一。文章最后通過實驗證明了在兩通道情況下本文所討論的降噪方法能夠在長287.5cm，寬175cm的區(qū)域內取得平均3.5dB(A)的降噪量，相比傳統(tǒng)有源降噪方法獲得了更廣的消聲區(qū)域。
[Abstract]:With the development of modern industry, the noise pollution is becoming more and more serious. The traditional noise control method has better effect on middle and high frequency noise control, but not on low frequency noise control. The method of active noise control brings hope to solve this problem, and it has the advantages of small volume, light weight, easy to control and so on. Many experts and scholars devote a lot of energy and time to the study of active noise control. At present, the active noise control technology is based on the linear adaptive filtering theory through different adaptive control algorithms to achieve noise reduction. However, this kind of algorithm is contradictory in terms of convergence rate and steady-state misalignment. Increasing the step size can accelerate the convergence rate and increase the steady-state misalignment at the same time. In general, this kind of adaptive algorithm can achieve better noise reduction for a single channel, but due to the large amount of computation and the slow convergence speed of multi-channel, it is difficult to give the number and position of the secondary sound source. It makes the multi-channel noise reduction difficult to achieve obvious results. In view of the shortcomings of existing control methods in multi-channel noise reduction, a blind signal separation technique is proposed for active noise control, and a two-channel de-noising model is used as an example. An analytical separation method for blind signals with correlation between two noise sources is introduced. Simulation results show the feasibility and accuracy of the proposed model. The two signals obtained after blind separation are two noise signals. Fourier transform is made for this signal, and then the main frequency components of the noise signal are obtained according to the spectrum diagram. The main frequency component information is used to construct the secondary sound source signal. The difference between the active noise control method based on blind signal separation and the traditional noise reduction method lies in the rejection of the traditional adaptive control method and the separation of mixed sound source signals by blind signal separation technology. For multi-channel noise reduction, it is precisely because of the blind separation technology that the exact information of each noise signal can be obtained, so that we can treat each channel in the multi-channel as a single channel to control. The number and location of secondary sound sources can be arranged pertinently, which is one of the innovations in this paper compared with traditional noise reduction methods. In the end of the paper, it is proved by experiments that the noise reduction method discussed in this paper can achieve an average noise reduction of 3.5 dB / A in a region with a length of 287.5 cm and a wide 175cm. Compared with the traditional active noise reduction method, the noise reduction method can obtain a wider range of noise suppression.
【學位授予單位】：內蒙古工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：TB535

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本文編號：1940316