基于传声器阵列的建模和定位算法研究
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摘要
传声器阵列可作为声通信系统的一个组成部分,用于拾取声信号。传声器阵列可用在语音增强、波束形成、系统辨识、去混响、语音识别与话者识别、声源定位与跟踪、回声抵消和语音分离等领域。本文基于传声器阵列研究混响和噪声环境中的多通道盲辨识、声源定位与跟踪。
为提高多通道盲辨识对非高斯噪声的鲁棒性,提出了一种归一化多通道频域自适应最小平均M-估计算法。不同于使用平方误差作为代价函数的传统方法,所提算法使用M-估计器构建多通道频域自适应滤波的代价函数,利用M估计器对非高斯噪声的不敏感性提高算法的性能。真实声环境的实验数据验证了提出的算法的优越性。
从非互信息角度提出了一种鲁棒的时延估计算法。将多个随机变量的联合熵分解成多个随机变量间的互信息和非互信息两类信息,提取出多个传声器信号间的非互信息构造时延估计器,获得对房间混响具有鲁棒性的算法。
将多通道互相关系数算法从仅使用空间信息推广到使用空时信息,提出了一种基于多通道空时预测的时延估计算法。理论分析表明所提算法对传声器信号起预白化作用,并对混响具有鲁棒性。进一步提出递归算法来降低所提算法的运算复杂度。仿真实验证实了提出的算法的优越性以及递归算法的有效性。
利用超心型指向性传声器建立了一种正方形阵列,对阵列的频响特性和指向性进行了分析,使其具有360度全方位拾音功能。在该阵列结构的基础上,提出了一种最大能量发言人自动跟踪算法,并通过实验验证了阵列和算法的有效性。
Microphone arrays, which are commonly employed to capture acoustic signals, are an important part of various acoustic communication systems. Microphone arrays are extensively applied to speech enhancement, beamforming, system identification, dereverberation, speech recognition and speaker recognition, sound source localization and tracking, acoustic echo cancellation, and speech separation. This dissertation focuses on research on blind multichannel identification, source localization and tracking based on microphone arrays.
Blind multichannel identification (BMCI) is to estimate the channel impulse responses of an unknown multichannel system based only on the output signals. To improve the resilience of BMCI to non-Gaussian noise, we propose a robust normalized multichannel frequency-domain least-mean M-estimate algorithm. Unlike the traditional approaches that use the squared error as the cost function, we use an M-estimator to form the cost function, which is shown robust to non-Gaussian noise with a symmetric α-stable distribution. Simulations demonstrate the superiority of the proposed algorithm.
To localize sound sources in room acoustic environments, time differences of arrival (TDOAs) between two or more microphone signals must be determined. In this dissertation, we partition the joint entropy of multiple random variables into two classes of information: mutual information shared by the multiple random variables and non-mutual information among them. We extract the non-mutual information among an array of microphones to estimate TDOA. Simulations in reverberant environments justify the effectiveness of the proposed algorithm.
The multichannel cross-correlation-coefficient (MCCC) algorithm, which is an extension of the traditional cross-correlation method from two-to multiple-channel cases, exploits spatial information among multiple microphones to improve the robustness of time delay estimation. In this dissertation, we propose a multichannel spatio-temporal prediction (MCSTP) algorithm, which can be viewed as a generalization of the MCCC principle from using only spatial information to using both spatial and temporal information. We also propose a recursive version of this new algorithm, which can achieve similar performance as MCSTP, but is computationally more efficient. Experimental results in reverberant and noisy environments demonstrate the advantages of this new method.
Finally, we use directional microphones to construct a square array, and analyze the frequency response and directionality of this array. To make the array capture the sound from all directions, we analyze how to design this array. Based on the square array, we propose a source tracking algorithm with the ability to track the speaker with the maximum speech power. Experimental results in anechoic and general rooms demonstrate the effectiveness of the algorithm.
为提高多通道盲辨识对非高斯噪声的鲁棒性,提出了一种归一化多通道频域自适应最小平均M-估计算法。不同于使用平方误差作为代价函数的传统方法,所提算法使用M-估计器构建多通道频域自适应滤波的代价函数,利用M估计器对非高斯噪声的不敏感性提高算法的性能。真实声环境的实验数据验证了提出的算法的优越性。
从非互信息角度提出了一种鲁棒的时延估计算法。将多个随机变量的联合熵分解成多个随机变量间的互信息和非互信息两类信息,提取出多个传声器信号间的非互信息构造时延估计器,获得对房间混响具有鲁棒性的算法。
将多通道互相关系数算法从仅使用空间信息推广到使用空时信息,提出了一种基于多通道空时预测的时延估计算法。理论分析表明所提算法对传声器信号起预白化作用,并对混响具有鲁棒性。进一步提出递归算法来降低所提算法的运算复杂度。仿真实验证实了提出的算法的优越性以及递归算法的有效性。
利用超心型指向性传声器建立了一种正方形阵列,对阵列的频响特性和指向性进行了分析,使其具有360度全方位拾音功能。在该阵列结构的基础上,提出了一种最大能量发言人自动跟踪算法,并通过实验验证了阵列和算法的有效性。
Microphone arrays, which are commonly employed to capture acoustic signals, are an important part of various acoustic communication systems. Microphone arrays are extensively applied to speech enhancement, beamforming, system identification, dereverberation, speech recognition and speaker recognition, sound source localization and tracking, acoustic echo cancellation, and speech separation. This dissertation focuses on research on blind multichannel identification, source localization and tracking based on microphone arrays.
Blind multichannel identification (BMCI) is to estimate the channel impulse responses of an unknown multichannel system based only on the output signals. To improve the resilience of BMCI to non-Gaussian noise, we propose a robust normalized multichannel frequency-domain least-mean M-estimate algorithm. Unlike the traditional approaches that use the squared error as the cost function, we use an M-estimator to form the cost function, which is shown robust to non-Gaussian noise with a symmetric α-stable distribution. Simulations demonstrate the superiority of the proposed algorithm.
To localize sound sources in room acoustic environments, time differences of arrival (TDOAs) between two or more microphone signals must be determined. In this dissertation, we partition the joint entropy of multiple random variables into two classes of information: mutual information shared by the multiple random variables and non-mutual information among them. We extract the non-mutual information among an array of microphones to estimate TDOA. Simulations in reverberant environments justify the effectiveness of the proposed algorithm.
The multichannel cross-correlation-coefficient (MCCC) algorithm, which is an extension of the traditional cross-correlation method from two-to multiple-channel cases, exploits spatial information among multiple microphones to improve the robustness of time delay estimation. In this dissertation, we propose a multichannel spatio-temporal prediction (MCSTP) algorithm, which can be viewed as a generalization of the MCCC principle from using only spatial information to using both spatial and temporal information. We also propose a recursive version of this new algorithm, which can achieve similar performance as MCSTP, but is computationally more efficient. Experimental results in reverberant and noisy environments demonstrate the advantages of this new method.
Finally, we use directional microphones to construct a square array, and analyze the frequency response and directionality of this array. To make the array capture the sound from all directions, we analyze how to design this array. Based on the square array, we propose a source tracking algorithm with the ability to track the speaker with the maximum speech power. Experimental results in anechoic and general rooms demonstrate the effectiveness of the algorithm.
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