摘要翻译：
声源波达方向(DOA)估计因其广泛的应用而成为信号处理的一个热门研究课题。利用球面传声器阵列(SMA)可以在球谐(SH)域进行DOA估计，而不会产生空间模糊。然而，环境混响和噪声会降低估计性能。本文提出了一种新的期望最大化(EM)算法，用于在SH域存在空间非均匀噪声的情况下L声源的确定性最大似然(ML)DOA估计。在此基础上，对信号模型在SH域内的确定性最大似然波达方向估计给出了一个新的闭式Cramer-Rao界(CRB)。该算法的主要思想是考虑接收信号在SH域的一般模型，将ML估计分解为两个步骤：期望和最大化步骤，从而降低了ML估计的复杂度。该算法将复杂度从2L维空间降低到L2维空间。仿真结果表明，在均方根误差(RMSE)方面，该算法的鲁棒性至少提高了6dB。此外，在较大的信噪比范围内，与现有的混响和噪声环境相比，该算法的RMSE非常接近CRB。
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英文标题：
《Robust Expectation-Maximization Algorithm for DOA Estimation of Acoustic
  Sources in the Spherical Harmonic Domain》
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作者：
Hossein Lolaee and Mohammad Ali Akhaee
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最新提交年份：
2017
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Audio and Speech Processing        音频和语音处理
分类描述：Theory and methods for processing signals representing audio, speech, and language, and their applications. This includes analysis, synthesis, enhancement, transformation, classification and interpretation of such signals as well as the design, development, and evaluation of associated signal processing systems. Machine learning and pattern analysis applied to any of the above areas is also welcome.  Specific topics of interest include: auditory modeling and hearing aids; acoustic beamforming and source localization; classification of acoustic scenes; speaker separation; active noise control and echo cancellation; enhancement; de-reverberation; bioacoustics; music signals analysis, synthesis and modification; music information retrieval;  audio for multimedia and joint audio-video processing; spoken and written language modeling, segmentation, tagging, parsing, understanding, and translation; text mining; speech production, perception, and psychoacoustics; speech analysis, synthesis, and perceptual modeling and coding; robust speech recognition; speaker recognition and characterization; deep learning, online learning, and graphical models applied to speech, audio, and language signals; and implementation aspects ranging from system architecture to fast algorithms.
处理代表音频、语音和语言的信号的理论和方法及其应用。这包括分析、合成、增强、转换、分类和解释这些信号，以及相关信号处理系统的设计、开发和评估。机器学习和模式分析应用于上述任何领域也是受欢迎的。感兴趣的具体主题包括：听觉建模和助听器；声波束形成与声源定位；声场景分类；说话人分离；有源噪声控制和回声消除；增强；去混响；生物声学；音乐信号的分析、合成与修饰；音乐信息检索；多媒体音频和联合音视频处理；口语和书面语建模、切分、标注、句法分析、理解和翻译；文本挖掘；言语产生、感知和心理声学；语音分析、合成、感知建模和编码；鲁棒语音识别；说话人识别与特征描述；应用于语音、音频和语言信号的深度学习、在线学习和图形模型；以及从系统架构到快速算法的实现方面。
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一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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英文摘要：
  The direction of arrival (DOA) estimation of sound sources has been a popular signal processing research topic due to its widespread applications. Using spherical microphone arrays (SMA), DOA estimation can be applied in the spherical harmonic (SH) domain without any spatial ambiguity. However, the environment reverberation and noise can degrade the estimation performance. In this paper, we propose a new expectation maximization (EM) algorithm for deterministic maximum likelihood (ML) DOA estimation of L sound sources in the presence of spatially nonuniform noise in the SH domain. Furthermore a new closed-form Cramer-Rao bound (CRB) for the deterministic ML DOA estimation is derived for the signal model in the SH domain. The main idea of the proposed algorithm is considering the general model of the received signal in the SH domain, we reduce the complexity of the ML estimation by breaking it down into two steps: expectation and maximization steps. The proposed algorithm reduces the complexity from 2L-dimensional space to L 2-dimensional space. Simulation results indicate that the proposed algorithm shows at least an improvement of 6dB in robustness in terms of root mean square error (RMSE). Moreover, the RMSE of the proposed algorithm is very close to the CRB compared to the recent methods in reverberant and noisy environments in the large range of signal to noise ratio.
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PDF链接：
https://arxiv.org/pdf/1711.01583