Combining SRP-PHAT and two Kinects for 3D Sound Source Localization

Seewald, Lucas Adams; Gonzaga, Luiz; Veronez, Maurício Roberto; Minotto, Vicente Peruffo; Jung, Cláudio Rosito

doi:10.1016/j.eswa.2014.05.033

Cited by 14 publications

(10 citation statements)

References 22 publications

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“…Absolute horizontal errors of the SSL method were lower than 2°, and the relative error was lower than 25.0%, within the angle measurement range of −30° to +30°. The SRP-PHAT algorithm has shown less than 4° average direction errors (horizontal and vertical) with a sound emitter played by a standard PC speaker at a distance of 1.0–3.6 m [ 24 ]. The played signal was white Gaussian noise at a 44.1 kHz sampling rate.…”

Section: Discussionmentioning

confidence: 99%

“…As a result, the Kinect sensor determined the direction from which the sound came. Although Kinect position estimations are unsatisfactory, its direction estimations are very accurate based on the principle of time-difference of arrival (TDOA) and the steered response power using the PHAse Transform (SRP-PHAT) localization algorithm [ 24 ]. The reference algorithm and Kinect beamforming algorithm both need a large amount of calculation and are not suitable for real-time signal processing, while the cross correlation (CC) algorithm can realise the LabVIEW real-time localisation function with a small amount of calculation.…”

Section: Methodsmentioning

confidence: 99%

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A Sound Source Localisation Analytical Method for Monitoring the Abnormal Night Vocalisations of Poultry

Lao

Teng

2018

Sensors

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Due to the increasing scale of farms, it is increasingly difficult for farmers to monitor their animals in an automated way. Because of this problem, we focused on a sound technique to monitor laying hens. Sound analysis has become an important tool for studying the behaviour, health and welfare of animals in recent years. A surveillance system using microphone arrays of Kinects was developed for automatically monitoring birds’ abnormal vocalisations during the night. Based on the principle of time-difference of arrival (TDOA) of sound source localisation (SSL) method, Kinect sensor direction estimations were very accurate. The system had an accuracy of 74.7% in laboratory tests and 73.6% in small poultry group tests for different area sound recognition. Additionally, flocks produced an average of 40 sounds per bird during feeding time in small group tests. It was found that, on average, each normal chicken produced more than 53 sounds during the daytime (noon to 6:00 p.m.) and less than one sound at night (11:00 p.m.–3:00 a.m.). This system can be used to detect anomalous poultry status at night by monitoring the number of vocalisations and area distributions, which provides a practical and feasible method for the study of animal behaviour and welfare.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

A Sound Source Localisation Analytical Method for Monitoring the Abnormal Night Vocalisations of Poultry

Lao

Teng

2018

Sensors

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“…Location-aware wearable haptics [7] require the system to robustly track users' positions; therefore, occlusion can cause interruptions to the interaction. Sound localization [8] can also be affected by occlusion, but the use of auxiliary Kinects could enable new types of Kinect-based sound localization solutions. In collaborative environments, such as the attention-and proximity-aware multiuser interface developed by Dostal et al [9], our occlusion-free joint data could help the system recognize otherwise absent gestures.…”

Section: Out Of Sight Toolkitmentioning

confidence: 99%

Out of sight: a toolkit for tracking occluded human joint positions

Quigley

Harris-Birtill

2016

Pers Ubiquit Comput

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Real-time identification and tracking of the joint positions of people can be achieved with off-the-shelf sensing technologies such as the Microsoft Kinect, or other camera-based systems with computer vision. However, tracking is constrained by the system's field of view of people. When a person is occluded from the camera view, their position can no longer be followed. Out of Sight addresses the occlusion problem in depth-sensing tracking systems. Our new tracking infrastructure provides human skeleton joint positions during occlusion, by combining the field of view of multiple Kinects using geometric calibration and affine transformation. We verified the technique's accuracy through a system evaluation consisting of 20 participants in stationary position and in motion, with two Kinects positioned parallel, 45, and 90 apart. Results show that our skeleton matching is accurate to within 16.1 cm (s.d. = 5.8 cm), which is within a person's personal space. In a realistic scenario study, groups of two people quickly occlude each other, and occlusion is resolved for 85% of the participants. A RESTful API was developed to allow distributed access of occlusion-free skeleton joint positions. As a further contribution, we provide the system as open source.

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“…In comparison with the implementation presented in (Seewald et al, 2014), we design our application to achieve maximum performance on GPUs making use of the Kepler architecture GK110 (K20, 2014) (See Appendix A for details). This architecture can be found on the Tegra K1 (TK1) systems-on-chip (SoC), embedded in the Jetson development kit (DevKit) (Jetson, 2015), and 100 it is becoming widespread in current mobile devices such as Google's Nexus 9 tablet (Nexus, 2015).…”

mentioning

confidence: 99%

“…In (Seewald et al, 2014), the SRP-PHAT algorithm is implemented over two Kinects for performing sound 65 source localization. In the same work, the algorithm only estimates the relative source direction instead of providing the absolute source position and the implementation is evaluated on different GPUs that belong to the old-fashioned…”

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confidence: 99%

On the performance of multi-GPU-based expert systems for acoustic localization involving massive microphone arrays

Belloch

González

Vidal

et al. 2015

Expert Systems with Applications

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ElsevierBelloch Rodríguez, JA.; Vidal Maciá, AM.; Cobos Serrano, M. (2015). On the performance of multi-GPU-based expert systems for acoustic localization involving massive microphone array. Expert Systems with Applications. 42 (13) AbstractSound source localization is an important topic in expert systems involving microphone arrays, such as automatic camera steering systems, human-machine interaction, video gaming or audio surveillance. The Steered Response Power with Phase Transform (SRP-PHAT) algorithm is a well-known approach for sound source localization due to its robust performance in noisy and reverberant environments. This algorithm analyzes the sound power captured by an acoustic beamformer on a defined spatial grid, estimating the source location as the point that maximizes the output power. Since localization accuracy can be improved by using high-resolution spatial grids and a high number of microphones, accurate acoustic localization systems require high computational power. Graphics Processing Units (GPUs) are highly parallel programmable co-processors that provide massive computation when the needed operations are properly parallelized. Emerging GPUs offer multiple parallelism levels; however, properly managing their computational resources becomes a very challenging task. In fact, management issues become even more difficult when multiple GPUs are * Corresponding author: Phone Number +34-655436190 Email addresses: jobelrod@iteam.upv.es (Jose A. Belloch), agonzal@dcom.upv.es (Alberto Gonzalez), avidal@dsic.upv.es (Antonio M. Vidal), maximo.cobos@uv.es (Maximo Cobos) Preprint submitted to Journal of L A T E X TemplatesFebruary 25, 2015 involved, adding one more level of parallelism. In this paper, the performance of an acoustic source localization system using distributed microphones is analyzed over a massive multichannel processing framework in a multi-GPU system. The paper evaluates and points out the influence that the number of microphones and the available computational resources have in the overall system performance. Several acoustic environments are considered to show the impact that noise and reverberation have in the localization accuracy and how the use of massive microphone systems combined with parallelized GPU algorithms can help to mitigate substantially adverse acoustic effects. In this context, the proposed implementation is able to work in real time with high-resolution spatial grids and using up to 48 microphones. These results confirm the advantages of suitable GPU architectures in the development of real-time massive acoustic signal processing systems.

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Combining SRP-PHAT and two Kinects for 3D Sound Source Localization

Cited by 14 publications

References 22 publications

A Sound Source Localisation Analytical Method for Monitoring the Abnormal Night Vocalisations of Poultry

A Sound Source Localisation Analytical Method for Monitoring the Abnormal Night Vocalisations of Poultry

Out of sight: a toolkit for tracking occluded human joint positions

On the performance of multi-GPU-based expert systems for acoustic localization involving massive microphone arrays

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