基于麥克風(fēng)陣列的近場聲源定位與跟蹤
發(fā)布時間：2018-08-23 15:55
【摘要】：隨著陣列信號處理技術(shù)的日漸成熟,基于麥克風(fēng)陣列的聲源定位與跟蹤現(xiàn)已逐漸得到比較廣泛的應(yīng)用。由于室內(nèi)聲源為寬帶非平穩(wěn)信號,傳統(tǒng)的窄帶信號DOA估計和跟蹤算法無法直接應(yīng)用于此,且現(xiàn)有算法通常具有較高的算法復(fù)雜度,所以基于麥克風(fēng)陣列的聲源定位和跟蹤仍然具有較大的改進空間。本文對基于麥克風(fēng)陣列的近場聲源DOA估計和跟蹤相關(guān)算法進行了研究和改進,主要研究內(nèi)容包括： 第一、綜合分析了語音信號的時頻特性,結(jié)合陣列信號處理中遠(yuǎn)場均勻線陣平面波信號接收模型研究了均勻線陣、均勻圓陣和任意拓?fù)浣Y(jié)構(gòu)的近場球面波信號接收模型。 第二、對麥克風(fēng)陣列接收到的數(shù)據(jù)進行包括預(yù)濾波、預(yù)加重、歸一化、加窗分幀和語音降噪等在內(nèi)的各種預(yù)處理和語音端點檢測。本文對語音降噪進行了重點研究,文中采用了自適應(yīng)小波分解層數(shù)選取和改進型閾值函數(shù)相結(jié)合的方法來提高小波語音去噪的性能。 第三、采用麥克風(fēng)均勻圓陣近場模型,對近場3D-MUSIC算法和寬帶聚焦3D-MUSIC算法進行了對比研究。針對均勻圓陣等傳統(tǒng)麥克風(fēng)陣列近場信號模型對聲源俯仰角估計產(chǎn)在角度模糊的缺陷,建立了新的麥克風(fēng)陣列模型；并在此基礎(chǔ)上針對寬帶聚焦3D-MUSIC算法中三維平均空間譜矩陣求解及譜峰搜索計算量大的問題,提出了分步降維估計法來減小算法計算量。最后,通過實驗仿真驗證了該方法在降低算法計算量的基礎(chǔ)上,依然保持了良好的DOA估計性能。 第四、將基于壓縮投影逼近子空間(PASTd)算法的信號DOA跟蹤,應(yīng)用到三維近場聲源跟蹤,并利用第三點所述的分步降維估計法來減小每幀數(shù)據(jù)DOA估計時的計算量。針對該算法跟蹤誤差較大或收斂速度較慢的缺點,提出了可變遺忘因子PASTd算法,最后通過實驗仿真驗證了改進算法良好的DOA跟蹤性能。
[Abstract]:With the development of array signal processing technology, sound source location and tracking based on microphone array has been widely used. Because the indoor sound source is a wideband non-stationary signal, the traditional narrowband signal DOA estimation and tracking algorithms can not be directly applied here, and the existing algorithms usually have high algorithm complexity. Therefore, the sound source location and tracking based on microphone array still has great improvement space. In this paper, the DOA estimation and tracking algorithms for near-field acoustic sources based on microphone array are studied and improved. The main research contents are as follows: first, the time-frequency characteristics of speech signals are analyzed synthetically. Combined with the plane wave reception model of far-field uniform linear array in array signal processing, the near-field spherical wave signal receiving model of uniform linear array, uniform circular array and arbitrary topology is studied. Secondly, all kinds of preprocessing and speech endpoint detection including pre-filtering, pre-weighting, normalization, windowed framing and speech denoising are carried out for the data received by the microphone array. This paper focuses on the research of speech denoising. In this paper, the adaptive wavelet decomposition layer selection and the improved threshold function are used to improve the performance of wavelet speech denoising. Thirdly, the near-field 3D-MUSIC algorithm and wideband focused 3D-MUSIC algorithm are compared by using the near-field model of microphone uniform circular array. A new microphone array model is proposed to solve the problem that the traditional near-field signal model of microphone array, such as uniform circular array, produces fuzzy angle to estimate pitch angle of sound source. On this basis, aiming at the problem of large amount of computation in solving the three-dimensional average spatial spectral matrix and searching the spectral peak in the wideband focused 3D-MUSIC algorithm, a fractional step reduced dimension estimation method is proposed to reduce the computational complexity of the algorithm. Finally, the experimental results show that the proposed method still maintains good DOA estimation performance on the basis of reducing the computational complexity of the algorithm. Fourthly, the signal DOA tracking based on compressed projection approximation subspace (PASTd) algorithm is applied to 3D near-field sound source tracking, and the fractional step dimensionality reduction method proposed in the third point is used to reduce the computational complexity of DOA estimation for each frame. A variable forgetting factor (PASTd) algorithm is proposed to overcome the disadvantages of large tracking error and slow convergence rate of the algorithm. Finally, the improved DOA tracking performance is verified by experimental simulation.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN912.3
本文編號：2199555
[Abstract]:With the development of array signal processing technology, sound source location and tracking based on microphone array has been widely used. Because the indoor sound source is a wideband non-stationary signal, the traditional narrowband signal DOA estimation and tracking algorithms can not be directly applied here, and the existing algorithms usually have high algorithm complexity. Therefore, the sound source location and tracking based on microphone array still has great improvement space. In this paper, the DOA estimation and tracking algorithms for near-field acoustic sources based on microphone array are studied and improved. The main research contents are as follows: first, the time-frequency characteristics of speech signals are analyzed synthetically. Combined with the plane wave reception model of far-field uniform linear array in array signal processing, the near-field spherical wave signal receiving model of uniform linear array, uniform circular array and arbitrary topology is studied. Secondly, all kinds of preprocessing and speech endpoint detection including pre-filtering, pre-weighting, normalization, windowed framing and speech denoising are carried out for the data received by the microphone array. This paper focuses on the research of speech denoising. In this paper, the adaptive wavelet decomposition layer selection and the improved threshold function are used to improve the performance of wavelet speech denoising. Thirdly, the near-field 3D-MUSIC algorithm and wideband focused 3D-MUSIC algorithm are compared by using the near-field model of microphone uniform circular array. A new microphone array model is proposed to solve the problem that the traditional near-field signal model of microphone array, such as uniform circular array, produces fuzzy angle to estimate pitch angle of sound source. On this basis, aiming at the problem of large amount of computation in solving the three-dimensional average spatial spectral matrix and searching the spectral peak in the wideband focused 3D-MUSIC algorithm, a fractional step reduced dimension estimation method is proposed to reduce the computational complexity of the algorithm. Finally, the experimental results show that the proposed method still maintains good DOA estimation performance on the basis of reducing the computational complexity of the algorithm. Fourthly, the signal DOA tracking based on compressed projection approximation subspace (PASTd) algorithm is applied to 3D near-field sound source tracking, and the fractional step dimensionality reduction method proposed in the third point is used to reduce the computational complexity of DOA estimation for each frame. A variable forgetting factor (PASTd) algorithm is proposed to overcome the disadvantages of large tracking error and slow convergence rate of the algorithm. Finally, the improved DOA tracking performance is verified by experimental simulation.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN912.3
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