Source Localization Research Articles

Neural Correlates of the Musicianship Advantage to the Cocktail Party Effect.

Prior research has indicated musicians show an auditory processing advantage in phonemic processing of language. The aim of the current study was to elucidate when in the auditory cortical processing stream this advantage emerges in a cocktail-party-like environment. Participants (n = 34) were aged 18-35 years and deemed to be either a musician (10+-year experience) or nonmusician (no formal training). EEG data were collected while participants were engaged in a phoneme discrimination task. During the task, participants were asked to discern auditory "ba" and "pa" phonemes in two conditions: one with competing speech (target with distractor [TD]) and one without competing speech (target only). Behavioral results showed that musicians discriminated phonemes better under the TD condition than nonmusicians, whereas no performance differences were observed during the target only condition. Analysis of the EEG ERP showed musicianship-based differences at both early (N1) and late (P3) processing stages during the TD condition. Specifically, musicians exhibited decreased neural activity during the N1 and increased neural activity during the P3. Source localization of the P3 showed that musicians increased activity in the right superior/middle temporal gyrus. Results from this study indicate that musicians have a phonemic processing advantage specifically when presented in the context of distraction, which arises from a shift in neural activity from early (N1) to late (P3) stages of cortical phonemic processing.

Impact Damage Localization in Composite Structures Using Data-Driven Machine Learning Methods.

Due to the uncertainty of material properties of plate-like structures, many traditional methods are unable to locate the impact source on their surface in real time. It is important to study the impact source-localization problem for plate structures. In this paper, a data-driven machine learning method is proposed to detect impact sources in plate-like structures and its effectiveness is tested on three plate-like structures with different material properties. In order to collect data on the localization of the impact source, four piezoelectric transducers and an oscilloscope were utilized to construct an experimental platform for impulse response testing. Meanwhile, the position of the impact source on the surface of the test plate is generated by manually releasing the steel ball. The eigenvalue of arrival time in the time domain signal is extracted to build data sets for machine learning. This paper uses the Back Propagation (BP) neural network to learn the difference in the arrival time of each sensor and predict the location of the impact source. The results demonstrate that the machine learning method proposed in this paper can predict the location of the impact source in the plate-like structure without relying on the material properties, with high test accuracy and robustness. The research work in this paper can provide experimental methods and testing techniques for locating impact damage in composite material structures.

Spatial Correlations Based Fault Tolerant Source Localization Using Wireless Sensor Networks

The source localization is a process to determine the location of a signal source based on measurements from sensor nodes or receivers that are spatially distributed in the area around the source. Localizing intruders and mobile users are some of the utilizations of wireless sensor networks (WSNs) based source localization problem. However, sensor faults and channel noise are the important issues that degrade the source localization performances of the systems. In this paper, we significantly study and improve our recent results; the representative fault tolerant hitting set and feature selection approaches for localizing a source in a decentralized WSN using the faulty sensor nodes and channel noise. First, we propose a technique to place some active sensor nodes in the sleep state such that the source localization performances of the hitting set and feature selection approaches do not significantly degrade. The proposed technique takes advantage of spatial correlations among sensor nodes, where nearby sensor nodes provide same decisions to the fusion center with a high probability. We observe that the performance gap for the results with and without sleeping sensor nodes decreases by increasing total sensor nodes. In addition, we improve our recent results by proposing a technique based on spatial correlations among sensor nodes to minimize the impact of channels’ noises, especially for a higher fault probability of sensor nodes. Finally, extensive simulations on synthetic and real-world data sets validate our theoretical results.

Virtualized Microphone Array for Moving Sources Mapping.

The acoustic analysis of a moving object, such as in pass-by or fly-over tests, is a very important and demanding issue. These types of analyses make it possible to characterize the machine in quite realistic conditions, but the typical difficulties related to source localization and characterization are usually exacerbated by the need to take into consideration and to compensate for the object movement. In this paper, a technique based on acoustic beamforming is proposed, which is applicable to all those cases where the object under investigation is moving. In the proposed technique, the object's movement is not regarded as a problem but as a resource, enabling a virtual increase in the number of microphone acquisitions. For a stationary acoustic emission from a moving object, each time segment of the acquired signal is treated as if it is coming from a microphone (virtual) positioned differently relative to the object's reference system. This paper describes the technique and presents examples of results obtained from both simulated and real signals. Performance analysis is conducted and discussed in detail.

Quantifying spectral information about source separation in multisource odour plumes.

Odours released by objects in natural environments can contain information about their spatial locations. In particular, the correlation of odour concentration timeseries produced by two spatially separated sources contains information about the distance between the sources. For example, mice are able to distinguish correlated and anti-correlated odour fluctuations at frequencies up to 40 Hz, while insect olfactory receptor neurons can resolve fluctuations exceeding 100 Hz. Can this high-frequency acuity support odour source localization? Here we answer this question by quantifying the spatial information about source separation contained in the spectral constituents of correlations. We used computational fluid dynamics simulations of multisource plumes in two-dimensional chaotic flow environments to generate temporally complex, covarying odour concentration fields. By relating the correlation of these fields to the spectral decompositions of the associated odour concentration timeseries, and making simplifying assumptions about the statistics of these decompositions, we derived analytic expressions for the Fisher information contained in the spectral components of the correlations about source separation. We computed the Fisher information for a broad range of frequencies and source separations for three different source arrangements and found that high frequencies were more informative than low frequencies when sources were close relative to the sizes of the large eddies in the flow. We observed a qualitatively similar effect in an independent set of simulations with different geometry, but not for surrogate data with a similar power spectrum to our simulations but in which all frequencies were a priori equally informative. Our work suggests that the high-frequency acuity of olfactory systems may support high-resolution spatial localization of odour sources. We also provide a model of the distribution of the spectral components of correlations that is accurate over a broad range of frequencies and source separations. More broadly, our work establishes an approach for the quantification of the spatial information in odour concentration timeseries.

Benefits of Cochlear Implantation for Older Adults With Asymmetric Hearing Loss.

FDA-approved indications for cochlear implantation include patients with severe-to-profound unilateral hearing loss (UHL) or asymmetric hearing loss (AHL); however, these indications are not covered for Medicare beneficiaries. We assessed the outcomes of cochlear implant (CI) use for older adults with UHL or AHL. Eighteen older adults (≥65 years of age at surgery) with UHL/AHL participated in a prospective, longitudinal investigation evaluating outcomes of CI use. Speech recognition for the affected ear was evaluated with consonant-nucleus-consonant (CNC) words. Spatial hearing was assessed with measures of sound source localization and sentence recognition in noise. The target sentence was presented from the front and the masker was either co-located with the target (SoNo), presented toward the affected ear (SoNci) or contralateral ear (SoNcontra). Perceived benefit was assessed with the Speech, Spatial, and Qualities of Hearing scale (SSQ) and the Tinnitus Handicap Inventory (THI). Participants experienced significant improvements with CI use for CNC words (mean [SD]; preop: 8% [10%], 1 yr: 51% [22%], 5 yr: 50% [19%]), masked sentence recognition (SoNcontra preop: 5% [6%], 1 yr: 22% [15%], 5 yr: 41% [14%]), and localization (preop: 76° [18°], 1 yr: 40° [11°], 5 yr: 41° [14°]),and reported significant improvements in hearing abilities (SSQ Spatial Hearing preop: 3 [1], 1 yr: 6 [2], 5 yr: 6 [2]) and tinnitus severity (THI preop: 16 [18], 1 yr: 4 [14], 5 yr: 6 [12]). Older adults with UHL/AHL experience significant improvements in speech recognition, spatial hearing, and subjective perceptions (e.g., hearing abilities and tinnitus severity) with a CI as compared to pre-operative abilities. 4 Laryngoscope, 135:352-360, 2025.

Mode-informed complex-valued neural processes for matched field processing.

A complex-valued neural process method, combined with modal depth functions (MDFs) of the ocean waveguide, is proposed to reconstruct the acoustic field. Neural networks are used to describe complex Gaussian processes, modeling the distribution of the acoustic field at different depths. The network parameters are optimized through a meta-learning strategy, preventing overfitting under small sample conditions (sample size equals the number of array elements) and mitigating the slow reconstruction speed of Gaussian processes (GPs), while denoising and interpolating sparsely distributed acoustic field data, generating dense field data for virtual receiver arrays. The predicted field is then integrated with the matched field processing (MFP) method for passive source localization. Validation on the SWellEx-96 waveguide shows significant improvements in localization performance and reduces sidelobes of ambiguity surface compared to traditional MFP and GP-based MFP. Moreover, the proposed kernel based on MDFs outperforms the Gaussian kernel in describing ocean waveguide characteristics. Because of the feature representation of multi-modal mapping, this kernel enhances acoustic field prediction performance and improves the accuracy and robustness of MFP. Simulated and real data are used to verify the validity.

Transcranial brain atlas based on photon measurement density function in a triple-parameter standard channel space.

Functional near-infrared spectroscopy (fNIRS) is a widely-used transcranial brain imaging technique in neuroscience research. Nevertheless, the lack of anatomical information from recordings poses challenges for designing appropriate optode montages and for localizing fNIRS signals to underlying anatomical regions. The photon measurement density function (PMDF) is often employed to address these issues, as it accurately measures the sensitivity of an fNIRS channel to perturbations of absorption coefficients at any brain location. However, existing PMDF-based localization methods have two limitations: (1) limited channel space, and (2) estimation based on a single standard head model, which usually differ anatomically from individuals. To overcome these limitations, this study proposes a continuous standard channel space for fNIRS and constructs a PMDF-based transcranial brain atlas (PMDF-TBA) by calculating PMDFs using MRI data from 48 adults. The PMDF-TBA contains group-averaged sensitivities of channels to gray matter and brain regions as defined in 3 atlases: Brodmann, AAL2, and LPBA40. We evaluated the prediction ability of PMDF-TBA for sensitivity of unseen individuals. The results show that it outperformed PMDFs based on single standard head models, making PMDF-TBA a more generalizable fNIRS spatial localization tool. Therefore, in the absence of individual sMRI data, PMDF-TBA can optimize optode montage design, enhance channel sensitivity in target brain regions, and assist in source localization for fNIRS data, thereby facilitating the application of fNIRS in neuroscience research.

A knowledge-driven framework for robotic odor source localization using large language models

A knowledge-driven framework for robotic odor source localization using large language models

Enhancing RSVP target recognition performance by multi-features extracted from EEG source localization and multi-granular information neural networks

Enhancing RSVP target recognition performance by multi-features extracted from EEG source localization and multi-granular information neural networks

Distributed Sound Source Localization Methods Using a Coarse Grid–Based Convolutional Neural Network

Distributed Sound Source Localization Methods Using a Coarse Grid–Based Convolutional Neural Network

Development of a machine learning-based radio source localization algorithm for tri-axial antenna configuration

Development of a machine learning-based radio source localization algorithm for tri-axial antenna configuration

Advancing Applications of Robot Audition Systems: Efficient HARK Deployment with GPU and FPGA Implementations

This paper proposes efficient implementations of robot audition systems, specifically focusing on deployments using HARK, an open-source software (OSS) platform designed for robot audition. Although robot audition systems are versatile and suitable for various scenarios, efficiently deploying them can be challenging due to their high computational demands and extensive processing times. For scenarios involving intensive high-dimensional data processing with large-scale microphone arrays, our generalizable GPU-based implementation significantly reduced processing time, enabling real-time Sound Source Localization (SSL) and Sound Source Separation (SSS) using a 60-channel microphone array across two distinct GPU platforms. Specifically, our implementation achieved speedups of 23.3× for SSL and 3.0× for SSS on a high-performance server equipped with an NVIDIA A100 80 GB GPU. Additionally, on the Jetson AGX Orin 32 GB, which represents embedded environments, it achieved speedups of 14.8× for SSL and 1.6× for SSS. For edge computing scenarios, we developed an adaptable FPGA-based implementation of HARK using High-Level Synthesis (HLS) on M-KUBOS, a Multi-Access Edge Computing (MEC) FPGA Multiprocessor System on a Chip (MPSoC) device. Utilizing an eight-channel microphone array, this implementation achieved a 1.2× speedup for SSL and a 1.1× speedup for SSS, along with a 1.1× improvement in overall energy efficiency.

Features of Planar Localization of Acoustic Emission Sources via the Inglada’s Triangulation Algorithm

This paper presents a methodology for enhancing the efficiency of acoustic emission (AE) source detection during planar localization using the Inglada’s algorithm. The study analyzes the main factors affecting the accuracy of AE source localization when using a standard planar localization approach. These factors include the threshold-based method of determining the signal registration time by AE sensors, which is based on detecting the moment when the rising wavefront voltage exceeds the discrimination threshold (uth), the signal sampling frequency (fd), and the influence of the medium’s dispersion properties on the attenuation of signal amplitude and wave propagation speed. To reduce the impact of these factors on the localization accuracy of AE sources, a novel methodology is proposed based on the use of correlation dependencies of AE pulse propagation speed on the amplitude of the recorded signals, as well as on accounting for the delay in the registration time of AE pulses during threshold detection. A series of preliminary experiments was conducted to implement the proposed methodology, where AE pulses were generated using an electronic simulator with a maximum amplitude level of um = 45—90 dB. The position of the AE pulse source varied in the range of 150 to 700 mm relative to the receiving sensors of the antenna array. As a result of applying the developed methodology, the probability of AE source detection increased to p = 0,71, compared to p = 0,36 when using the standard approach.

The Effect of Ear Covering on Sound Localization and Speech Intelligibility in Operating Room.

To determine the effect of the surgical bouffant cap on hearing, sound localization, and speech intelligibility in the operating room (OR). Covering of the ear during surgical procedures has been promoted to prevent surgical site infection (SSI) from ear pathogens. However, the potential impact of ear covering on hearing in the OR has not been studied. Twenty participants with normal hearing underwent auditory testing with and without surgical attire (bouffant cap and surgical mask). Auditory threshold testing was performed with warble tones in a sound-treated booth. In the speech intelligibility test, participants were instructed to identify the last word of Speech Perception in Noise (SPIN) sentences (high and low predictability) in OR background noise. In the sound source localization test, participants were asked to localize a speech source amidst OR background noise. Threshold measures showed no significant effect of wearing surgical attire on detection thresholds. Wearing surgical attire significantly impaired speech intelligibility (p < 0.05). Sound localization was not statistically significant. The use of the surgical bouffant cap and mask makes it more difficult to understand speech in the presence of OR noise. This could lead to miscommunication and impact surgical outcome; thus, ear covering should be avoided. N/A Laryngoscope, 2024.

Локализация движущегося шумового источника в мелководной акватории с нерегулярной батиметрией

The article presents the results of a numerical experiment on reconstructing the parameters of a moving underwater noise source in a shallow water area with irregular bathymetry in the form of a coastal wedge. The source localization problem is solved using holographic processing of noise signals. The case of horizontal refraction of sound field modes is considered. A single vector-scalar receiver is used as a receiving system.

Localization of infrasonic sources via Bayesian back projection

Summary A Bayesian framework is investigated for event-specific localization of infrasonic sources using back projection ray tracing. Direction-of-arrival information from array-based detection analysis is used to initialize a back projection ray path originating from the detecting array location and quantifying propagation characteristics from hypothetical source locations. The Fisher statistic, computed from the array’s beam coherence, is mapped into uncertainty in the launch angles of the ray path. Auxiliary parameters previously introduced for solving the Transport equation to compute geometric spreading along ray paths are used to map uncertainty in the ray launch angles into spatial and temporal uncertainties in the ray path. An atmospheric ensemble approach is applied to account for atmospheric uncertainty, and the relation between uncertainties in the atmospheric state and confidence in estimated localization are evaluated using several ensembles with specified variances. The method is evaluated using a synthetic event in the western United States constructed via forward propagation simulations as well as a single-station, multi-arrival detection from a surface explosion in the western United States. Localization results using this event-specific approach are more accurate and exhibit improved precision than existing Bayesian localization methods that leverage generalized, pre-computed propagation statistics.

Multiple Sound Sources Localization Using Sub-Band Spatial Features and Attention Mechanism

Multiple Sound Sources Localization Using Sub-Band Spatial Features and Attention Mechanism

Challenges in sound source localization on complex plate structures

Challenges in sound source localization on complex plate structures

A Review of Neuromorphic Sound Source Localization and Echolocation-Based Navigation Systems

The development of positioning systems has been significantly advanced by a combination of technological innovations, such as improved sensors, signal processing, and computational power, alongside inspiration drawn from biological mechanisms. Although vision is the main means for positioning oneself—or elements relative to oneself—in the environment, other sensory mediums provide additional information, and may even take over when visibility is lacking, such as in the dark or in troubled waters. In particular, the auditory system in mammals greatly contributes to determining the location of sound sources, as well as navigating or identifying objects’ texture and shape, when combined with echolocation behavior. Taking further inspiration from the neuronal processing in the brain, neuromorphic computing has been studied in the context of sound source localization and echolocation-based navigation, which aim at better understanding biological processes or reaching state-of-the-art performances in energy efficiency through the use of spike encoding. This paper sets out a review of these neuromorphic sound source localization, sonar- and radar-based navigation systems, from their earliest appearance to the latest published works. Current trends and possible future directions within this scope are discussed.