775 results about "Acoustic source localization" patented technology

The invention discloses an electromechanical equipment noise reduction method based on machine learning, and relates to the field of electromechanical equipment noise reduction, and the method comprises the steps: collecting sound pressure and vibration signals during the operation of electromechanical equipment, and collecting the operation state data of the electromechanical equipment; sparse reconstruction is carried out on the noise features; carrying out sound source localization on the reconstructed noise features by using a beam forming algorithm to obtain a noise distribution diagram; constructing a noise reduction model based on deep learning to obtain noise signals after noise reduction; extracting the frequency of the noise signal in a preset frequency range through a peak search algorithm; analyzing the acquired running state data by using a time-varying linear predictive coding algorithm TVLPC to obtain a noise frequency offset caused by the abnormal running state of the electromechanical equipment; generating an optimal solution by using a particle swarm algorithm; and according to the optimal solution of noise control, noise dominant frequency components are extracted for separation, and a plurality of phase-reversal noise reduction control signals are obtained. Aiming at low noise reduction precision of electromechanical equipment in the prior art, the noise reduction control precision is improved.

The invention relates to an intelligent microphone pickup and speech enhancement method, system and device, and the method comprises the steps: obtaining an audio signal collected by a dual-silicon microphone array, and carrying out the cross-correlation analysis of the audio signal, and obtaining a corresponding human voice signal correlation feature; performing sound source positioning analysis on the audio signal based on the human voice signal correlation feature to obtain a corresponding target human voice signal; performing frequency characteristic analysis on the target human voice signal, and performing segmented dynamic gain processing on the signal according to a preset frequency band range to obtain a corresponding voice enhancement signal; performing noise component adaptive filtering processing on the target human voice signal to obtain a corresponding noise suppression parameter; and inputting the speech enhancement signal and the noise suppression parameter into a preset echo cancellation model for joint optimization to obtain a corresponding output human voice signal. According to the invention, the coupling problem of multiple acoustic interferences can be effectively solved.

The invention relates to a microphone array sound source positioning method and system based on cross-correlation-beam forming closed-loop optimization, and belongs to the technical field of sound source positioning. The method comprises the following steps: collecting multichannel sound signals through a microphone array and preprocessing the multichannel sound signals to extract time-frequency features and suppress noise interference; time delay information among the microphones is estimated by adopting a generalized cross-correlation phase transformation algorithm, and an optimization strategy is introduced to improve estimation stability and anti-interference performance; enhancing the target sound source signal in combination with a minimum variance undistorted response beam forming algorithm and an adaptive Kalman filtering mechanism; constructing a closed-loop feedback optimization mechanism based on the beam output signal to realize feedback adjustment; and adopting a hybrid network architecture, taking the beam output signal amplitude spectrum as input, and outputting the frequency spectrum or mask of the obtained target sound source signal. The method has the advantages of high calculation efficiency, high positioning precision and strong anti-interference capability, and is suitable for real-time acoustic signal processing in a complex environment.

The invention relates to the technical field of animal identity recognition, in particular to an animal voiceprint monitoring method and device, a medium and a product, and the method comprises the steps: carrying out the sound source orientation of an activity sound when the existence of the activity sound is detected, controlling a sound collection device to face the direction where the activity sound is emitted, and carrying out the audio collection. Then, the audio data is preprocessed, voiceprint feature extraction is carried out based on the target audio data, a multi-dimensional voiceprint feature vector is determined, and the multi-dimensional voiceprint feature vector can comprehensively and meticulously display multi-aspect sound characteristics of the target audio data. And finally, matching with each voiceprint model in an animal voiceprint model library is carried out based on the multi-dimensional voiceprint feature vector, and animal information is determined. Through sound source positioning, the audio data is less interfered by background noise and irrelevant sound, the quality of the audio data is remarkably improved by executing the preprocessing operation, animal voiceprint monitoring is executed by using the high-quality target audio data, and the accuracy of animal voiceprint monitoring is improved.

The invention relates to a multi-element microphone array sound source localization method based on multistage signal preprocessing and subspace spectrum optimization, and belongs to the technical field of acoustic detection. Aiming at the problems of poor noise immunity, weak multi-sound-source resolution capability and low calculation efficiency of the existing sound source positioning technology, a triple signal preprocessing and subspace collaborative optimization scheme is provided; firstly, incoherent noise is suppressed through phase coherent filtering, a signal is reconstructed through principal component analysis, and phase deviation is calibrated through fundamental frequency; then constructing a guiding matrix and decomposing a noise subspace, and extracting a coarse positioning result; and finally, high-precision angle optimization is realized based on a chaos initialization differential evolution algorithm, and the efficiency is improved by combining a dynamic search range and an early stop mechanism. According to the method, the anti-interference capability in a low signal-to-noise ratio environment is remarkably enhanced, the problems of missing detection and false detection during dense distribution of multiple sound sources are effectively solved, meanwhile, the positioning precision and the real-time performance are considered, and the method is suitable for acoustic fault detection of complex scenes such as power transmission line inspection.

The invention discloses an immersed tunnel leakage voiceprint recognition system based on deep learning. The system comprises an acoustic signal acquisition module, a signal preprocessing module, a voiceprint feature extraction module, a deep learning recognition module and a leakage positioning and early warning module. According to the invention, the leakage water flow sound and the structural damage elastic wave signal of the immersed tunnel are comprehensively considered, the multi-dimensional voiceprint feature extraction and deep learning model are fused, and the adaptive signal noise reduction and sound source positioning technology is combined, so that the high-precision monitoring and real-time early warning of the leakage of the immersed tunnel are realized; the accuracy and real-time performance of immersed tunnel leakage nondestructive monitoring are improved, and powerful support is provided for follow-up risk prediction and early repair.

The invention belongs to the technical field of sound source positioning, and particularly relates to a sound source orientation identification method based on a three-array-element miniature microphone array. According to the method, sound source localization is converted into a deep learning classification task by extracting a microphone array sound intensity feature map. The method comprises the following steps: firstly, on the basis of a three-array-element differential microphone array, extracting orthogonal sound intensity features and converting the orthogonal sound intensity features into an RGB sound intensity spectrogram; secondly, designing a new lightweight neural network to meet the requirements of sound source localization and deployment; and finally, the horizontal plane sound source orientation is divided into different angle categories, a sound intensity spectrogram data set is made and sent to a neural network for training and testing, and a sound source orientation recognition result is obtained. Compared with the prior art, the feature map and the network model provided by the invention have better robustness in a high reverberation environment, the azimuth recognition angle precision is high, the calculation complexity is low, and the method is not influenced by voice characteristics.

The invention relates to the technical field of submarine cable fault positioning, in particular to a submarine cable fault positioning system based on underwater beacons. The method comprises the steps that an underwater acoustic beacon array unit monitors and collects submarine cable fault sound wave signals in real time based on underwater acoustic beacons; the fault signal processing analysis unit extracts submarine cable fault sound wave signal parameters based on a non-point source space sound source waveform analysis model, and generates multi-parameter fault sound source feature vectors; the sound source positioning analysis algorithm unit performs three-dimensional inversion positioning based on the multi-parameter fault sound source feature vector and the arrival time difference of the submarine cable fault sound wave signal to the underwater acoustic beacon, and obtains the spatial position information of the submarine cable fault point; and the data communication alarm management unit sends the spatial position information of the submarine cable fault point to a shore-based operation and maintenance system. The submarine cable fault positioning method is used for realizing a submarine cable fault positioning technology for quickly and accurately positioning and identifying spatial extension characteristics in a complex submarine environment.

The invention discloses an unmanned aerial vehicle risk assessment method and system based on a behavior recognition mode. The method comprises the steps of obtaining a target acoustic signal and a target image sequence of an enemy unmanned aerial vehicle; analyzing and processing the target acoustic signal to obtain sound source positioning information; analyzing and processing the target image sequence to obtain optical flow data; fusing the sound source positioning information and the optical flow data to obtain target fusion data; inputting the target fusion data into a target neural network for identification to obtain a target behavior mode of the hostile unmanned aerial vehicle; calculating the target behavior mode and the current sensor data to obtain probability distribution data of candidate behavior modes corresponding to the target behavior mode; and inputting the probability distribution data into a target risk assessment model for assessment to obtain a target risk level. The method can improve the risk assessment accuracy of the hostile unmanned aerial vehicle, facilitates the formulation of targeted coping strategies and defense measures, and can be widely applied to the technical field of security defense.

The invention relates to the technical field of power systems, in particular to a transformer substation low-frequency noise source positioning method. The method comprises the following steps: obtaining a three-dimensional environment model of each structure in a transformer substation, and obtaining a geometric body topological relation matrix in the three-dimensional environment model; performing field measurement on the transformer substation to obtain noise measurement data; and performing outer convolution fusion on noise images between adjacent units according to the geometric topological relation matrix and the local noise image data, and iteratively spreading the noise images to the whole transformer substation area to generate initial sound source distribution estimation data of the whole transformer substation. The sound energy flow is utilized to analyze and identify the source position, the static source and the moving source are separated and tracked in combination with direction estimation and trajectory reconstruction, the static source can be accurately positioned, meanwhile, the motion trajectory of the moving source is obtained, and the separation efficiency is improved.

The invention discloses a bird sound event detection method and system oriented to national key protection bird monitoring, and the method comprises the steps: collecting bird sound data and environment noise data of national key protection birds, and constructing a bird sound spectrogram data set and a noisy bird sound data set; constructing a bird sound noise reduction model and a target detection model; training and jointly optimizing a bird sound noise reduction model and a target detection model according to the bird sound spectrogram data set and the noisy bird sound data set; an image-audio mapping module is constructed and used for converting an image detection result of the target detection model into a sound event detection result of the audio; and obtaining to-be-detected bird sound data, performing retrieval and sound source localization on the target bird sound according to the bird sound noise reduction model, the target detection model and the image-audio mapping module, and determining species, time frequency and direction information of the target bird sound. The method can improve the detection effect of the field bird sound audio, further improves the classification precision, and can be widely applied to the technical field of acoustic information processing.

The invention relates to the technical field of signal processing, in particular to a sound source localization method and system based on improved adaptive beam forming. A microphone array is used for collecting sound source signals, and a signal receiving model is established; designing an improved adaptive filtering beam forming algorithm, and optimizing a cost function; solving an optimal weighting vector through a minimization cost function, and introducing an adaptive updating mechanism to dynamically adjust the weighting vector; selecting a reference microphone, calculating time difference of arrival and distance difference of other microphones, and establishing a distance difference equation from the sound source to the receiver; and solving a sound source coordinate and calculating the distance between the center of the microphone array and the sound source. According to the invention, through innovative algorithm design and a self-adaptive mechanism, the precision and stability of sound source localization are significantly improved, and especially, the method shows strong anti-interference capability and adaptability in a complex noise environment. The method can effectively improve the equipment maintenance efficiency in the substation equipment noise monitoring, reduces the fault risk, and provides reliable guarantee for the safe operation of a power grid.

The invention provides a single-microphone multi-array pickup method and system based on sound attenuation simulation, and the method comprises the steps: obtaining an original audio signal recorded by a single microphone and sound source direction information, and carrying out the sound attenuation simulation based on the sound source direction information and a preset virtual microphone array orientation parameter, calculating the included angle between the sound source direction and each virtual microphone; generating an audio intensity attenuation coefficient of each virtual microphone according to the included angle; and acting the audio intensity attenuation coefficient on the original audio signal to generate multi-channel audio data simulating the multi-microphone array. By adopting the method, the response difference of different microphone positions to the sound source can be simulated, the pickup result of the multi-microphone array is obtained, and high hardware cost and complex installation flow caused by actual deployment of a complex microphone array are avoided; and low-cost and high-diversity training data sources are provided for sound source localization, noise suppression and other models based on deep learning.

The invention discloses a sound source localization method based on a distributed array, and relates to the field of sound source localization. A distributed array and a centralized array are combined, the horizontal position information of a sound source is determined through a distributed array algorithm, and the vertical position information of the sound source is determined through a centralized array algorithm. Compared with other sound source localization methods, the sound source localization method has the advantages that coarse localization is conducted firstly, then fine localization is conducted, the CLEAN-SC algorithm is used for optimizing the wave beams, the characteristics of low calculation complexity and high resolution of the CLEAN-SC algorithm are fully utilized, and the sound source localization method with more excellent performance is achieved.

The invention relates to the technical field of sound beam control, and provides a sound beam forming pointing control method based on crowd recognition and related equipment thereof. The method comprises the following steps: performing time-space synchronous acquisition and calibration on multi-modal sensor data to obtain a time-space aligned original data set, and performing layered signal enhancement on the original data set to obtain a visual feature tensor and an audio feature tensor; dense crowd detection and tracking are carried out according to the visual feature tensor to obtain a target crowd space-time trajectory matrix, and sound source localization and beam weight calculation are carried out in combination with the audio feature tensor to obtain a beam forming weight vector; performing multi-constraint dynamic optimization in combination with environmental parameters in the multi-modal sensor data to obtain an adaptive control parameter set, and finally performing real-time beam forming in combination with a loudspeaker sequence to obtain a directional sound field output signal. According to the invention, target detection, sound source localization and dynamic adaptive control are combined on the multi-source data, so that the beam direction is dynamically adjusted.

The invention discloses a road obstacle recognition method based on machine vision, and the method comprises the steps: carrying out the precise positioning of the spatial position of an obstacle through a multi-sensor information fusion algorithm according to the azimuth and distance information of the obstacle estimated through a sound source positioning algorithm and the position information outputted by an obstacle detection model, and improving the positioning precision; the method comprises the following steps: acquiring guide mark information, such as road signs and lane lines, extracting guide mark features through a semantic segmentation algorithm, carrying out dynamic fusion on the guide mark features and an obstacle recognition result, and improving the accuracy and robustness of obstacle recognition by using prior information; in order to solve the problem of insufficient parallax analysis precision in long-distance, high-speed motion and other scenes, a monocular depth estimation algorithm based on deep learning is introduced, pixel-level depth information is predicted from a single image through a convolutional neural network, and the precision of obstacle distance estimation is improved.

The invention relates to the technical field of building structure health monitoring, in particular to an assembly type building detection method and system, and the method comprises the steps: collecting a strong interference signal generated by an external impact source of a building, and obtaining the sound source positioning information of the strong interference signal, converging the sound source positioning information of the strong interference signal on a preset three-dimensional geometric model aligned with a coordinate system to generate a real-time interference source distribution map; in the conventional detection mode, after a to-be-distinguished target acoustic signal is detected, obtaining a theoretical acoustic signal arrival time sequence based on sound source positioning information of at least one strong interference signal in the real-time interference source distribution diagram; comparing the actual acoustic signal arrival time sequence of the target acoustic signal with the theoretical acoustic signal arrival time sequence to obtain a comparison result; and judging whether the target acoustic signal is damaged in the building or not according to a comparison result. Real damage events from the interior of the structure can be effectively separated and confirmed, and the accuracy and reliability of detection are improved.

The invention provides a sound source localization method based on an improved particle swarm algorithm. Comprising the following steps: selecting a sound source positioning scene, acquiring a measurement data set by using a microphone array, and establishing a mathematical model; initializing the positions and speeds of the particles, and calculating the fitness value of each particle to evaluate the distance between the current position of the particle and the target solution; setting an inertia weight to control the influence of the current speed of the particles on the new speed, and setting an individual learning factor and a social learning factor to control and coordinate the behaviors of the particles in the search space; setting an iteration control condition, combining with a hybrid optimization algorithm, and utilizing a boundary processing strategy to carry out iteration updating of particles; and after iteration updating is completed, error analysis is performed on a sound source positioning result, and the particle swarm optimization algorithm is continuously optimized by using an optimization strategy according to an analysis result. The method can effectively improve the sound source positioning precision and algorithm performance, is suitable for a complex acoustic environment, and provides an efficient and feasible solution for the application in the acoustic field.

The invention provides an examination room multi-source data fusion abnormal behavior intelligent analysis method and system, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting examination room video and audio data, and carrying out the multi-view skeleton point fusion and sound source positioning to extract features; constructing a hidden Markov model and kernel density estimation to carry out abnormal behavior identification; calculating a seat correlation degree based on a spatial weight coefficient and wavelet decomposition to identify multi-person cooperative cheating; and early warning information is pushed in real time. According to the invention, the cheating behavior identification accuracy is improved, and effective detection of multi-person cooperative cheating behaviors is realized.

The invention relates to the technical field of voice recognition, in particular to an implementation method and device of a multi-channel voiceprint recognition system, and the implementation method comprises the steps of multi-channel data acquisition and synchronization, signal preprocessing and enhancement, feature extraction and fusion, model training, real-time deployment and adaptive optimization. Compared with the problems that a traditional multichannel voiceprint recognition system depends on a fixed beam forming algorithm and an independent clock synchronization module, the synchronization error is large, manual parameter adjustment is needed for noise suppression, and generalization is poor, hardware-level clock synchronization is achieved through a PTP protocol, and the accuracy of noise suppression is improved. The method combines an end-to-end neural network to automatically learn noise distribution and a sound source space position, dynamically generates a beam forming weight, can improve the voice quality in a complex noise scene without manual intervention, remarkably reduces the interference of a synchronization error on sound source positioning, and enables the precision and stability of far-field voice enhancement to reach a new level.

The invention discloses an all-weather sound source positioning system and method based on multi-sensor fusion, and the method comprises the steps: firstly, obtaining acoustic sensing data, millimeter wave radar data and infrared thermal imaging data, and enabling each data to have a collection timestamp; then, cross-modal time alignment processing is carried out on the multi-source data to map the multi-source data to a unified time reference, and multi-modal fusion features under a unified time axis are obtained; for each time point in the time-aligned multi-modal fusion features, according to the confidence coefficient of the data of each sensor, adaptive weighted fusion is carried out on the data of different modals, and fused common feature representation is generated; and finally, time sequence modeling and joint reasoning are carried out based on the common feature representations of a plurality of continuous time points, and continuous position information of the sound source in the three-dimensional space is regressed. According to the method, high-precision positioning of three-dimensional positions of a plurality of sound sources is realized, so that the accuracy and the stability of sound source positioning are improved in a complex environment and an all-weather condition.

The invention relates to the technical field of environmental noise monitoring, and discloses a road traffic noise intelligent monitoring and three-dimensional sound field reconstruction system, which comprises an acoustic sensor module, a data collection and storage module, a data analysis and evaluation module, a three-dimensional sound field construction and display module and a traffic flow feature library. According to the system, a differential geometry principle is adopted, a sound field is regarded as a Riemannian manifold with a local microstructure, and accurate description of an irregular sound field is realized through a curvature self-adaptive sound field manifold construction technology; introducing a covariant derivative in Riemannian geometry, and constructing a sound propagation model adapted to a complex road environment; and realizing hierarchical decomposition and reconstruction of the sound field by using a multi-scale analysis theory. According to the method, the sound source positioning precision and the calculation efficiency are improved, seamless analysis from microcosmic to macroscopic is realized, an innovative solution is provided for traffic and noise collaborative management, and intelligent traffic and environmental noise management are effectively supported.

The invention relates to the field of real-time translation, and provides a real-time duplex translation method based on multichannel parallel processing and a corresponding product, and the method comprises the steps: collecting multipath voice signals of at least two user groups in real time through a group of audio collection modules; dynamically adjusting beam forming parameters of each audio acquisition module in one group of audio acquisition modules based on a sound source positioning result, and feeding back the beam forming parameters to the corresponding audio acquisition modules; monitoring the voice activity of each audio acquisition module corresponding to each audio channel; when it is monitored that the voice activity of any audio channel reaches a preset condition, automatically activating the translation processing flow of the audio channel and keeping the monitoring state of the other audio channels; a parallel processing mechanism is adopted for voice signals of the activated audio channels, and meanwhile real-time translation of the currently activated audio channels and voice activity monitoring of the other audio channels are executed; and transmitting a translation result of the current speaking user to other users participating in dialogue in the user group to realize synchronous coordination of multichannel data.

The invention relates to the technical field of voice processing, in particular to a sound source direction-finding positioning method and system for a complex environment, and the method comprises the steps: serializing the sound intensity of each microphone, and calculating a maximum receiving interval according to the distance between adjacent microphones, the sound transmission speed and the sampling frequency; the method comprises the following steps: determining a short-time audio similarity through moving a sound source signal sequence, extracting a sound source synchronization sequence and a sound source same-frequency sequence, and further evaluating a tone quality deviation degree; optionally selecting a reference microphone, and comparing the reference microphone with a sound source synchronization sequence of other microphones to obtain a time delay data length and a sound source similar sequence; based on the arrangement values of the same position elements of the sound source similar sequence, a sound intensity distribution sequence is obtained, and the sound intensity interference degree of each microphone is obtained; and determining a fixed beam direction and calculating the distance between the reference microphone and the sound source. The invention aims to improve the accuracy of sound source positioning.

A sound source determining method and system and an electronic device are disclosed. The sound source determining method includes: obtaining initial audio information collected in real time; performing audio recognition processing on the initial audio information to obtain an audio recognition result; using the initial audio information corresponding to the audio recognition result as target audio information in a case that the audio recognition result indicates that the initial audio information meets a preset audio recognition condition; performing audio information activity detection on the target audio information to obtain target audio activity information; and performing sound source positioning on a sound producing object corresponding to the target audio activity information according to sound source positioning parameters corresponding to the target audio activity information to obtain target position information of the sound producing object.

The invention discloses a sound source positioning and intelligent tracking method and system based on a multi-sensor array, and relates to the field of digital signal processing, and the sound source positioning and intelligent tracking method comprises the steps: system starting and environment signal collection; preprocessing and optimizing the signal; sound source positioning calculation; intelligent dynamic tracking; data transmission and real-time display; and data analysis and remote control. According to the invention, high-precision sound source localization is realized through the improved TDOA algorithm and deep learning optimization, and intelligent dynamic tracking of the sound source is ensured by combining fuzzy control and the PID algorithm optimized by the neural network; meanwhile, signal processing is optimized through technologies such as adaptive filtering and automatic gain control, and accurate input is provided; the system supports a multi-mode sensor array, and the tracking precision and adaptability are enhanced in combination with sound source signals and environmental parameters; and the upper computer has remote control and data analysis functions, so that the operability and the data management capability of the system are improved.

The invention discloses a spatial projection regularization method for sound source localization and sound field reconstruction and a related device, and belongs to the technical field of sound source localization. Specifically, according to the method, an equivalent source method model is utilized, and an external radiation sound field of a sound source to be measured is simulated through a series of virtual sources. Then, according to the first sound pressure transfer matrix from the virtual source arrangement surface to the holographic measuring point plane and the sound pressure of the holographic measuring point, a space projection regularization algorithm is used for solving the inverse problem, and therefore the equivalent source intensity is obtained; and then, reconstructing an external radiation sound field of the sound source to be detected by using the second sound pressure transfer matrix and the equivalent source intensity. And finally, by taking the maximum sound pressure amplitude as a standard, screening field points in an external radiation sound field so as to determine the position of the sound source to be detected. According to the space projection regularization algorithm, truncation processing of the vector space is carried out with the projection size of the measurement information in the vector space as the criterion, real information is reserved, and noise interference is restrained to the maximum extent.

The invention provides an out-of-vehicle voice control method and device, a storage medium and electronic equipment, and relates to the technical field of artificial intelligence, and the method comprises the steps: firstly obtaining audio signals collected by a microphone array on a vehicle for different areas outside the vehicle; the audio signals are preprocessed; performing sound source positioning and sound source separation according to the preprocessed audio signal to obtain a multi-channel audio signal; then performing wake-up word recognition based on the multi-channel audio signal, and determining a target channel where the wake-up word is located; and executing a corresponding vehicle voice control command according to the audio signal corresponding to the target channel. According to the method and the device, the audio signals are acquired through the microphone arrays in different areas around the vehicle, and the multichannel audio signals are obtained through preprocessing, sound source positioning and separation, so that accurate wake-up word recognition and voice control command execution are realized, outdoor complex environmental noise can be effectively shielded, and the accuracy of out-of-vehicle voice control is greatly improved.

The invention relates to the technical field of voice communication, and provides an online conference transcription method capable of realizing intelligent conference summary, which comprises the following steps: collecting audio data of each speaker in a conference process in real time through a microphone array, and carrying out primary processing on the collected audio signals; performing sound source positioning on the audio signal and determining a source direction of the audio signal according to a sound source positioning result; performing voiceprint recognition on the audio signal to extract voice spectrum features of the speaker, and marking the voiceprint of the speaker according to a voiceprint recognition result; and the weighted audio signal is used for character transcription, and the speaker is positioned to generate a complete conference summary. The problems that in a traditional audio transfer system, multiple speakers cannot be accurately recognized, speech content cannot be distinguished, and manual proofreading work cannot be reduced are effectively solved, and the accuracy and efficiency of conference recording are improved.

Acoustic signals from an acoustic event are captured via sensing nodes of sensor group(s) that comprise a group of sensing nodes at a location comprising spatial boundaries. Each of the sensing nodes comprise a sensor area. Each of the sensor group(s) is based on: range limits of each of the sensing nodes; shared sensing areas of the sensing nodes; and intersections between the sensor area for each of the sensing nodes and the spatial boundaries. Solutions(s) are generated by processing the acoustic signals. The solution(s) indicate the location or trajectory of the acoustic event. A strength of solution compliance value for at least one of the solution(s) is determined. A refined solution is generated employing: sensor contributions of sensing nodes; and the strength of solution compliance value with the spatial boundaries and at least one of the solution(s). A report is created comprising the location or trajectory of the acoustic event.