Báo cáo hóa học: Research Article Constant False Alarm Rate Sound Source Detection with Distributed Microphones

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Báo cáo hóa học: " Research Article Constant False Alarm Rate Sound Source Detection with Distributed Microphones"Hindawi Publishing CorporationEURASIP Journal on Advances in Signal ProcessingVolume 2011, Article ID 656494, 12 pagesdoi:10.1155/2011/656494Research ArticleConstant False Alarm Rate Sound Source Detection withDistributed Microphones Kevin D. Donohue, Sayed M. SaghaianNejadEsfahani, and Jingjing Yu Department of Electrical and Computer Engineering, University of Kentucky, Lexington, KY 40506, USA Correspondence should be addressed to Kevin D. Donohue, donohue@engr.uky.edu Received 5 March 2010; Accepted 24 January 2011 Academic Editor: Sven Nordholm Copyright © 2011 Kevin D. Donohue et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Applications related to distributed microphone systems are typically initiated with sound source detection. This paper introduces a novel method for the automatic detection of sound sources in images created with steered response power (SRP) algorithms. The method exploits the near-symmetric coherent power noise distribution to estimate constant false-alarm rate (CFAR) thresholds. Analyses show that low-frequency source components degrade CFAR threshold performance due to increased nonsymmetry in the coherent power distribution. This degradation, however, can be offset by partial whitening or increasing differential path distances between the microphone pairs and the spatial locations of interest. Experimental recordings are used to assess CFAR performance subject to variations in source frequency content and partial whitening. Results for linear, perimeter, and planar microphone geometries demonstrate that experimental false-alarm probabilities for CFAR thresholds ranging from 10−1 and 10−6 are limited to within one order of magnitude when proper filtering, partial whitening, and noise model parameters are applied.1. Introduction or PHAT-β [11, 12], outperforms the PHAT for a variety of signal source types typically found in speech. DetectionAutomatic sound source detection with distributed micro- performance was analyzed using receiver operating charac-phone systems is relevant for enhancing applications such teristic (ROC) curve areas, which reflect overall detectionas teleconferencing [1, 2], speech recognition [3–6], talker and false-alarm performance without regard to a threshold.tracking [7], and beamforming [8]. Many of these applica- A CFAR threshold is typically estimated based on a probabilistic model of the noise-only distribution, such thattions involve the detection and location of sound sources.For example, an automatic minute-taking application must parameters are estimated from the local data to maintaindetect and locate active voices before beamforming to a fixed probability of false alarm over nonstationarities.create independent channels for each speaker. Failure to Adaptive thresholding algorithms based on a CFAR approachdetect active sound sources or false detections will degrade are common in radar and other applications, where largeperformance. This paper, therefore, introduces a method amounts of nonstationary noise samples are available [13– 15]. The CFAR algorithm presented here differs from previ-for automatically detecting sound sources using a variant ofthe steered response power (SRP) algorithm and applying a ous approaches in that it uses coherent power. The coherentnovel constant false-alarm rate (CFAR) threshold algorithm. power is the sum of correlations between signals from all Recent work has shown the SRP algorithm to be robust distinct microphone pairs focused on a point of interestin reverberant and multiple speaker environments when (where no microphone signal is correlated with itself). Thisused in conjunction with a phase transform (PHAT) [9, can be computed by subtracting the power of each individual10]. The PHAT whitens the signals by setting the Fourier microphone signal from the usual SRP value to create anmagnitudes to unity while maintaining the original phase. acoustic image with positive and negative values. WhileA detailed analysis based on detection performance showed common CFAR approaches use the cells or pixels (whichthat a variant of the PHAT, referred to as partial whitening are all positive) in the test pixel neighborhood to estimate2 EURASIP Journal on Advances in Signal Processingthe FA threshold, the approach described in this paper geometries used in the experiments. Frequency ranges for each array are derived for achieving sufficient distributiondistinguishes itself by exploiting a distribution similaritybetween the positive and negative coherent noise pixels. symmetry. Section 4 directly analyzes the noise distribu-The CFAR threshold is computed only from the absolute tions with the Weibull distribution for various frequencyvalues of the negative pixels in the test pixel ne ...