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Investigation of sampling frequency requirements for acoustic source localisation using wireless sensor networks

机译：使用无线传感器网络进行声源定位的采样频率要求的调查

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摘要

There is considerable interest in the use of wireless sensor networks (WSNs) for distributed sound capture and acoustic source localisation (ASL) where array elements are spaced over a large area. High sampling rates, such as digital audio at 44.1 kHz, pose a major challenge for efficient wireless personal area network (WPAN) standards such as IEEE 802.15.4 (Zigbee) with an absolute maximum data throughput of 250 kbps. This paper investigates the effect of sampling frequency on the accuracy of time delay estimation using different algorithms in the time domain, such as basic cross correlation (BCC) and generalised cross correlation (GCC), frequency and content based features such as envelope, including generalised phase spectrum (GPS) and envelope-GPS (EGPS). Experimental and simulation studies have been undertaken which show that frequency domain and content based features algorithms can achieve more accurate time delay estimation at low sampling frequencies than time domain algorithms if the appropriate signal contents are extracted. Therefore they are more appropriate for wireless ASL applications.
机译：使用无线传感器网络（WSN）进行分布式声音捕获和声源定位（ASL）引起了极大的兴趣，其中阵列元素在大面积上间隔开。高采样率（例如44.1 kHz的数字音频）对有效的无线个人区域网（WPAN）标准（例如，IEEE 802.15.4（Zigbee））具有250 kbps的绝对最大数据吞吐量提出了重大挑战。本文研究了采样频率对时域中使用不同算法（例如基本互相关（BCC）和广义互相关（GCC），基于频率和内容的特征（例如包络）在内的时延估计的准确性的影响）相位频谱（GPS）和包络GP​​S（EGPS）。已经进行的实验和仿真研究表明，如果提取了适当的信号内容，则与时域算法相比，基于频域和内容的特征算法可以在低采样频率下实现更准确的时延估计。因此，它们更适合于无线ASL应用。

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