聲源DOA估計(jì)中的TDOA-DOA映射方法研究
發(fā)布時(shí)間：2018-08-04 07:46
【摘要】：聲源波達(dá)方向(Direction Of Arrival,DOA)估計(jì)作為麥克風(fēng)陣列信號(hào)處理中的一項(xiàng)關(guān)鍵技術(shù),在視頻會(huì)議系統(tǒng)、故障檢測(cè)、醫(yī)療診斷、軍事等許多領(lǐng)域都有廣泛應(yīng)用�；诙嗤ǖ赖竭_(dá)時(shí)間差(Time Differences Of Arrival,TDOA)的方法是聲源DOA估計(jì)中的一種重要方法。然而當(dāng)前研究工作主要集中在TDOA獲取,而對(duì)TDOA-DOA映射方法研究較少�；谧钚《酥С窒蛄炕貧w機(jī)(Least Squares Support Vector Regression,LS-SVR)的TDOA-DOA映射方法有較好的聲源DOA估計(jì)效果,但其研究并不全面。本文針對(duì)基于LS-SVR的TDOA-DOA映射方法,從LS-SVR中的核函數(shù)選取、多核LS-SVR構(gòu)造以及稀疏化分析等方面進(jìn)行了深入研究。此外,本文提出一種基于稀疏表示理論的無(wú)需調(diào)節(jié)參數(shù)的TDOA-DOA映射方法。本文的主要工作有:1)由于不同核函數(shù)具有不同的映射性能,因而本文研究了徑向基核、多項(xiàng)式核以及線性核這三種常見(jiàn)核函數(shù)構(gòu)造的LS-SVR在混響和噪聲環(huán)境中的聲源DOA估計(jì)性能,并與最小二乘映射方式進(jìn)行了比較。研究結(jié)果表明,采用徑向基核函數(shù)具有更高的估計(jì)性能。2)針對(duì)估計(jì)時(shí)延在混響較為嚴(yán)重的環(huán)境中出現(xiàn)離群值的問(wèn)題,本文根據(jù)TDOA-DOA的映射特點(diǎn),提出一種基于中值濾波的TDOA處理方法以消除離群值。研究結(jié)果表明,采用該方法后,在混響較為嚴(yán)重的環(huán)境中聲源DOA映射性能得到了有效提升。3)為了進(jìn)一步提升聲源DOA估計(jì)性能,本文結(jié)合多核學(xué)習(xí)理論以及K-means聚類算法,提出了基于聚類方法的多核LS-SVR映射方法。仿真結(jié)果表明,多核LS-SVR的性能要優(yōu)于單核LS-SVR以及最小二乘法;一般情況下,核的個(gè)數(shù)越多,多核LS-SVR的性能越好,并且混響時(shí)間越大,多核的性能優(yōu)勢(shì)體現(xiàn)得越明顯。4)針對(duì)LS-SVR映射方法中訓(xùn)練集存在冗余這一問(wèn)題,本文將基于最小支持權(quán)重的剪枝稀疏方法運(yùn)用到聲源DOA估計(jì)中,分別對(duì)單核和多核LS-SVR映射方法進(jìn)行了稀疏化分析。研究結(jié)果表明,與基本LS-SVR相比,稀疏LS-SVR方法不僅能保持良好的DOA估計(jì)性能,而且有效減小了測(cè)試時(shí)的運(yùn)算量。5)提出了一種基于稀疏表示理論的無(wú)需調(diào)節(jié)參數(shù)的TDOA-DOA映射方法。在此基礎(chǔ)上,為進(jìn)一步降低運(yùn)算量,本文應(yīng)用一種雙步網(wǎng)格搜索方法來(lái)匹配TDOA向量和數(shù)據(jù)字典。研究結(jié)果表明,與傳統(tǒng)的無(wú)需調(diào)節(jié)參數(shù)的映射方法相比,該算法存在一定的性能優(yōu)勢(shì)。
[Abstract]:As a key technology in microphone array signal processing, acoustic source DOA estimation (DOA) estimation is widely used in many fields such as video conferencing system, fault detection, medical diagnosis, military and so on. The method based on multi-channel time-of-arrival (Time Differences Of ArrivalTDOA) is an important method in sound source DOA estimation. However, the current research focuses on TDOA acquisition, but less on TDOA-DOA mapping methods. The TDOA-DOA mapping method based on least squares support vector regression machine (Least Squares Support Vector) has a good effect on sound source DOA estimation, but its research is not comprehensive. In this paper, the method of TDOA-DOA mapping based on LS-SVR is studied in detail from the aspects of kernel function selection, multi-core LS-SVR construction and sparse analysis in LS-SVR. In addition, this paper presents a TDOA-DOA mapping method based on sparse representation theory without adjusting parameters. The main work of this paper is: (1) because different kernel functions have different mapping performance, this paper studies the DOA estimation performance of LS-SVR in reverberation and noise environments with three common kernel functions, radial basis kernel, polynomial kernel and linear kernel. And compared with the least square mapping method. The results show that the radial basis function has higher estimation performance (.2). In order to solve the problem of outliers in the reverberation environment, the mapping characteristics of TDOA-DOA are discussed in this paper. A TDOA processing method based on median filter is proposed to eliminate outliers. The results show that the performance of sound source DOA mapping is improved by using this method in a more serious reverberation environment. In order to further improve the performance of sound source DOA estimation, this paper combines multi-core learning theory and K-means clustering algorithm. A multi-core LS-SVR mapping method based on clustering method is proposed. The simulation results show that the performance of multi-core LS-SVR is better than that of single core LS-SVR and least square method. In general, the more the number of cores, the better the performance and reverberation time of multi-core LS-SVR. The more obvious the performance advantage of multi-kernel is, the more obvious is the redundancy of training set in LS-SVR mapping method. In this paper, the pruning sparse method based on minimum support weight is applied to the sound source DOA estimation. The single core and multi-core LS-SVR mapping methods are analyzed respectively. The results show that compared with the basic LS-SVR, the sparse LS-SVR method can not only maintain good DOA estimation performance, but also reduce the computational complexity of the test effectively.) A new TDOA-DOA mapping method based on sparse representation theory without adjusting parameters is proposed. On this basis, a two-step grid search method is applied to match TDOA vectors and data dictionaries in order to further reduce the computational complexity. The results show that the proposed algorithm has some performance advantages compared with the traditional mapping method without adjusting parameters.
【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN911.23
本文編號(hào)：2163138
[Abstract]:As a key technology in microphone array signal processing, acoustic source DOA estimation (DOA) estimation is widely used in many fields such as video conferencing system, fault detection, medical diagnosis, military and so on. The method based on multi-channel time-of-arrival (Time Differences Of ArrivalTDOA) is an important method in sound source DOA estimation. However, the current research focuses on TDOA acquisition, but less on TDOA-DOA mapping methods. The TDOA-DOA mapping method based on least squares support vector regression machine (Least Squares Support Vector) has a good effect on sound source DOA estimation, but its research is not comprehensive. In this paper, the method of TDOA-DOA mapping based on LS-SVR is studied in detail from the aspects of kernel function selection, multi-core LS-SVR construction and sparse analysis in LS-SVR. In addition, this paper presents a TDOA-DOA mapping method based on sparse representation theory without adjusting parameters. The main work of this paper is: (1) because different kernel functions have different mapping performance, this paper studies the DOA estimation performance of LS-SVR in reverberation and noise environments with three common kernel functions, radial basis kernel, polynomial kernel and linear kernel. And compared with the least square mapping method. The results show that the radial basis function has higher estimation performance (.2). In order to solve the problem of outliers in the reverberation environment, the mapping characteristics of TDOA-DOA are discussed in this paper. A TDOA processing method based on median filter is proposed to eliminate outliers. The results show that the performance of sound source DOA mapping is improved by using this method in a more serious reverberation environment. In order to further improve the performance of sound source DOA estimation, this paper combines multi-core learning theory and K-means clustering algorithm. A multi-core LS-SVR mapping method based on clustering method is proposed. The simulation results show that the performance of multi-core LS-SVR is better than that of single core LS-SVR and least square method. In general, the more the number of cores, the better the performance and reverberation time of multi-core LS-SVR. The more obvious the performance advantage of multi-kernel is, the more obvious is the redundancy of training set in LS-SVR mapping method. In this paper, the pruning sparse method based on minimum support weight is applied to the sound source DOA estimation. The single core and multi-core LS-SVR mapping methods are analyzed respectively. The results show that compared with the basic LS-SVR, the sparse LS-SVR method can not only maintain good DOA estimation performance, but also reduce the computational complexity of the test effectively.) A new TDOA-DOA mapping method based on sparse representation theory without adjusting parameters is proposed. On this basis, a two-step grid search method is applied to match TDOA vectors and data dictionaries in order to further reduce the computational complexity. The results show that the proposed algorithm has some performance advantages compared with the traditional mapping method without adjusting parameters.
【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN911.23
【參考文獻(xiàn)】
相關(guān)期刊論文 前1條
1 譚穎;殷福亮;李細(xì)林;;改進(jìn)的SRP-PHAT聲源定位方法[J];電子與信息學(xué)報(bào);2006年07期
，本文編號(hào)：2163138
本文鏈接：http://sikaile.net/kejilunwen/wltx/2163138.html
最近更新
教材專著
