CABE : a cloud-based acoustic beamforming emulator for FPGA-based sound source localization

Microphone arrays are gaining in popularity thanks to the availability of low-cost microphones. Applications including sonar, binaural hearing aid devices, acoustic indoor localization techniques and speech recognition are proposed by several research groups and companies. In most of the available implementations, the microphones utilized are assumed to offer an ideal response in a given frequency domain. Several toolboxes and software can be used to obtain a theoretical response of a microphone array with a given beamforming algorithm. However, a tool facilitating the design of a microphone array taking into account the non-ideal characteristics could not be found. Moreover, generating packages facilitating the implementation on Field Programmable Gate Arrays has, to our knowledge, not been carried out yet. Visualizing the responses in 2D and 3D also poses an engineering challenge. To alleviate these shortcomings, a scalable Cloud-based Acoustic Beamforming Emulator (CABE) is proposed. The non-ideal characteristics of microphones are considered during the computations and results are validated with acoustic data captured from microphones. It is also possible to generate hardware description language packages containing delay tables facilitating the implementation of Delay-and-Sum beamformers in embedded hardware. Truncation error analysis can also be carried out for fixed-point signal processing. The effects of disabling a given group of microphones within the microphone array can also be calculated. Results and packages can be visualized with a dedicated client application. Users can create and configure several parameters of an emulation, including sound source placement, the shape of the microphone array and the required signal processing flow. Depending on the user configuration, 2D and 3D graphs showing the beamforming results, waterfall diagrams and performance metrics can be generated by the client application. The emulations are also validated with captured data from existing microphone arrays.</jats:p

Multiverse: Mobility pattern understanding improves localization accuracy

Department of Computer Science and EngineeringThis paper presents the design and implementation of Multiverse, a practical indoor localization system that can be deployed on top of already existing WiFi infrastructure. Although the existing WiFi-based positioning techniques achieve acceptable accuracy levels, we find that existing solutions are not practical for use in buildings due to a requirement of installing sophisticated access point (AP) hardware or special application on client devices to aid the system with extra information. Multiverse achieves sub-room precision estimates, while utilizing only received signal strength indication (RSSI) readings available to most of today's buildings through their installed APs, along with the assumption that most users would walk at the normal speed. This level of simplicity would promote ubiquity of indoor localization in the era of smartphones.ope

Low-frequency broadband sound source localization using an adaptive normal mode back-propagation approach in a shallow-water ocean

Author Posting. © Acoustical Society of America, 2012. This article is posted here by permission of Acoustical Society of America for personal use, not for redistribution. The definitive version was published in Journal of the Acoustical Society of America 131 (2012): 1798-1813, doi:10.1121/1.3672643.A variety of localization methods with normal mode theory have been established for localizing low frequency (below a few hundred Hz), broadband signals in a shallow water environment. Gauss-Markov inverse theory is employed in this paper to derive an adaptive normal mode back-propagation approach. Joining with the maximum a posteriori mode filter, this approach is capable of separating signals from noisy data so that the back-propagation will not have significant influence from the noise. Numerical simulations are presented to demonstrate the robustness and accuracy of the approach, along with comparisons to other methods. Applications to real data collected at the edge of the continental shelf off New Jersey, USA are presented, and the effects of water column fluctuations caused by nonlinear internal waves and shelfbreak front variability are discussed.The SW06 experiment was supported by the Office of Naval Research

Proceedings of the EAA Spatial Audio Signal Processing symposium: SASP 2019

International audienc

Three dimensional volcano-acoustic source localization at Karymsky Volcano, Kamchatka, Russia

Thesis (M.S.) University of Alaska Fairbanks, 2013We test two methods of 3-D acoustic source localization on volcanic explosions and small-scale jetting events at Karymsky Volcano, Kamchatka, Russia. Recent infrasound studies have provided evidence that volcanic jets produce low-frequency aerodynamic sound (jet noise) similar to that from man-made jet engines. Man-made jets are known to produce sound through turbulence along the jet axis, but discrimination of sources along the axis of a volcanic jet requires a network of sufficient topographic relief to attain resolution in the vertical dimension. At Karymsky Volcano, the topography of an eroded edifice adjacent to the active cone provided a platform for the atypical deployment of five infrasound sensors with intra-network relief of ~600 m in July 2012. A novel 3-D inverse localization method, srcLoc, is tested and compared against a more common grid-search semblance technique. Simulations using synthetic signals indicate that srcLoc is capable of determining vertical source locations for this network configuration to within �150 m or better. However, srcLoc locations for explosions and jetting at Karymsky Volcano show a persistent overestimation of source elevation and underestimation of sound speed by an average of ~330 m and 25 m/s, respectively. The semblance method is able to produce more realistic source locations by fixing the sound speed to expected values of 335 - 340 m/s. The consistency of location errors for both explosions and jetting activity over a wide range of wind and temperature conditions points to the influence of topography. Explosion waveforms exhibit amplitude relationships and waveform distortion strikingly similar to those theorized by modeling studies of wave diffraction around the crater rim. We suggest delay of signals and apparent elevated source locations are due to altered raypaths and/or crater diffraction effects. Our results suggest the influence of topography in the vent region must be accounted for when attempting 3-D volcano acoustic source localization. Though the data presented here are insufficient to resolve noise sources for these jets, which are much smaller in scale than those of previous volcanic jet noise studies, similar techniques may be successfully applied to large volcanic jets in the future.1. Introduction -- 2. Jet noise -- 2.1. Man-made jet noise -- 2.2. Volcanic jet-noise -- 3. Campaign and data -- 3.1. Karymsky Volcano -- 3.2. Acoustic data -- 4. Localization methods -- 4.1. Localization primer -- 4.2. Inverse locator: srcLoc -- 4.3. Forward locator: semblance -- 5. Results and discussion -- 5.1. srcLoc -- 5.2. Semblance -- 5.3. Discussion of errors -- 6. Conclusions -- Reference

Spike-Timing-Based Computation in Sound Localization

Spike timing is precise in the auditory system and it has been argued that it conveys information about auditory stimuli, in particular about the location of a sound source. However, beyond simple time differences, the way in which neurons might extract this information is unclear and the potential computational advantages are unknown. The computational difficulty of this task for an animal is to locate the source of an unexpected sound from two monaural signals that are highly dependent on the unknown source signal. In neuron models consisting of spectro-temporal filtering and spiking nonlinearity, we found that the binaural structure induced by spatialized sounds is mapped to synchrony patterns that depend on source location rather than on source signal. Location-specific synchrony patterns would then result in the activation of location-specific assemblies of postsynaptic neurons. We designed a spiking neuron model which exploited this principle to locate a variety of sound sources in a virtual acoustic environment using measured human head-related transfer functions. The model was able to accurately estimate the location of previously unknown sounds in both azimuth and elevation (including front/back discrimination) in a known acoustic environment. We found that multiple representations of different acoustic environments could coexist as sets of overlapping neural assemblies which could be associated with spatial locations by Hebbian learning. The model demonstrates the computational relevance of relative spike timing to extract spatial information about sources independently of the source signal