球面可转向矢量差分麦克风阵列

IF 4.1 2区 计算机科学 Q1 ACOUSTICS
Hüseyin Hacıhabiboğlu
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引用次数: 0

摘要

差分传声器阵列(DMA)使用多个全向传声器合成高阶传声器指向性模式。在最基本的形式中,它们可用于获得固定指向性或水平转向波束成形器,并能满足某些约束条件。我们提出了一种矢量差分传声器阵列(VDMA),它在三个维度上具有频率和方向可变的转向性。所提出的设计包括压力和粒子速度传感器,它们被放置在平面上的一个圆形星座上,可以提取声场的三阶球形谐波分解。然后,可以利用这种分解来获得球面方向不变的转向波束。演示了最大指向性系数(MaxDF)指向性模式的合成。得出了拟议阵列白噪声增益(WNG)的闭式表达式。分析了拟议设计对噪声的鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spherically Steerable Vector Differential Microphone Arrays
Differential microphone arrays (DMAs) use multiple omnidirectional microphones for synthesising higher-order microphone directivity patterns. In their most basic form, they can be used to obtain fixed-directivity or horizontally steerable beamformers that can satisfy certain constraints. We propose a vector differential microphone array (VDMA) which is frequency- and direction-invariantly steerable in three dimensions. The proposed design comprises pressure and particle velocity sensors positioned on a circular constellation in a plane and allows extracting the third-order spherical harmonic decomposition of the sound field. This decomposition can then be used to obtain spherically direction-invariant steered beams. Synthesis of a maximum directivity factor (MaxDF) directivity pattern is demonstrated. A closed-form expression for the proposed array's white noise gain (WNG) is derived. The robustness of the proposed design to noise is analysed.
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来源期刊
IEEE/ACM Transactions on Audio, Speech, and Language Processing
IEEE/ACM Transactions on Audio, Speech, and Language Processing ACOUSTICS-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
11.30
自引率
11.10%
发文量
217
期刊介绍: The IEEE/ACM Transactions on Audio, Speech, and Language Processing covers audio, speech and language processing and the sciences that support them. In audio processing: transducers, room acoustics, active sound control, human audition, analysis/synthesis/coding of music, and consumer audio. In speech processing: areas such as speech analysis, synthesis, coding, speech and speaker recognition, speech production and perception, and speech enhancement. In language processing: speech and text analysis, understanding, generation, dialog management, translation, summarization, question answering and document indexing and retrieval, as well as general language modeling.
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