Experimental performance analysis of sound source detection with SRP PHAT-&amp;#x03B2;

Ramamurthy, Anand; Unnikrishnan, Harikrishnan; Donohue, Kevin D.

doi:10.1109/secon.2009.5174117

Cited by 7 publications

(11 citation statements)

References 8 publications

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“…Simulated array recordings allowed for testing performance over a broad range of source placements and conditions. Details of the simulator are described in [7,[15][16][17]. …”

Section: Experiments and Resultsmentioning

confidence: 99%

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Computational strategy for accelerating robust sound source detection in dynamic scenes

Donohue

Griffioen

2014

Ieee Southeastcon 2014

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Efficient sound source detection and location with microphone arrays is important for many applications, including teleconferencing, surveillance, and smart rooms. While the steered response power algorithms exhibit robust performance relative to other approaches, their applications are limited by the high computational load required. For dynamic auditory scenes, the entire space must be scanned at regular intervals due to moving sound sources switching between active and inactive states. This paper introduces a time segmentation and parallelization strategy to speed up the steered response power algorithm for dynamic auditory scenes with multiple speech sources. The primary application targeted by this work is for immersive arrays and off-line auditory scene analysis with beamforming for speaker separation in cocktail party environments. Results from a Monte Carlo simulation with 6 speech sources in a mildly reverberant environment demonstrate a speed-up factor of 45, with a modest loss in the number of detections and a significant reduction in anomalous detections.Experimental results with real recordings demonstrate a performance consistent with those of the simulation.

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“…Simulated array recordings allowed for testing performance over a broad range of source placements and conditions. Details of the simulator are described in [7,[15][16][17]. …”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Typically the phase only transform (PHAT) is applied here. The algorithm used in this paper applies a slight variation referred to as the PHAT-,B [16] [16,17].…”

Section: Introductionmentioning

confidence: 99%

Computational strategy for accelerating robust sound source detection in dynamic scenes

Donohue

Griffioen

2014

Ieee Southeastcon 2014

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“…where β can be chosen on the interval [0 1] to achieve various degrees of whitening, where β equal to zero results in no whitening, and β equal to 1 results in total whitening as in the PHAT [9,10]. Other values of β result in partial whitening as in the case of the PHAT-β [11,12]. The SRP pixel value, corresponding to r i , is computed from the signal power at the lth time frame…”

Section: Steered Response Coherent Powermentioning

confidence: 99%

“…where W i and G k (ω) are the mean values of W i and G k (ω) over all microphone pairs and FOV points. Equation (12) shows that the two complex exponential factors have the potential to drive the expected value to zero. The factor with the differential path lengths from the noise sources to the microphone pairs will be referred to as the noise-path factor.…”

Section: Expected Value Of Noise Pixelsmentioning

confidence: 99%

“…The PHAT whitens the signals by setting the Fourier magnitudes to unity while maintaining the original phase. A detailed analysis based on detection performance showed that a variant of the PHAT, referred to as partial whitening or PHAT-β [11,12], outperforms the PHAT for a variety of signal source types typically found in speech. Detection performance was analyzed using receiver operating characteristic (ROC) curve areas, which reflect overall detection and false-alarm performance without regard to a threshold.…”

Section: Introductionmentioning

confidence: 99%

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Constant False Alarm Rate Sound Source Detection with Distributed Microphones

Donohue

SaghaianNejadEsfahani

2011

EURASIP J. Adv. Signal Process.

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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.

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