585 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 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 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 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 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 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.

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.

The invention provides a gas leakage detection method, device, system and equipment, a medium and a program product, and relates to the technical field of equipment detection. The method comprises the following steps: acquiring a sound source positioning coordinate of a sound source positioning point; the sound source positioning coordinates are used for representing the relative position of the sound source positioning point relative to the sound source positioning device; converting the sound source positioning coordinate into a sound source image coordinate in a target image coordinate system; based on the sound source image coordinates and a plurality of to-be-detected areas of a to-be-detected device, determining a gas leakage detection result of the to-be-detected device; the plurality of to-be-detected areas are image areas under the target image coordinate system; wherein the plurality of to-be-detected areas are obtained by carrying out image segmentation on a scene image of a scene where the to-be-detected device is located, and the plurality of to-be-detected areas are all areas where the to-be-detected device may generate gas leakage. According to the invention, accurate positioning of gas leakage points can be realized, so that the accuracy of gas leakage detection is improved.

The invention relates to a transformer abnormal sound source positioning method and system, and belongs to the technical field of power equipment state evaluation, and the method comprises the steps: constructing a Bayesian neural network embedded with a voiceprint physical mechanism, and enabling a sound wave propagation equation to serve as a physical constraint to be embedded into the Bayesian neural network; reconstructing the voiceprint signal by adopting a compressed sensing technology to obtain a reconstructed voiceprint field; designing a multi-task objective function including data fitting, physical constraint and positioning loss, and optimizing data fitting, physical constraint and positioning precision to obtain a trained Bayesian neural network; based on a gradient sound source inversion positioning algorithm and the trained Bayesian neural network, sparse regularization is combined to obtain a prediction result of accurate positioning; and a prediction result is visualized to a three-dimensional model of the transformer, and the position of an abnormal sound source is visually displayed. According to the method, the limitation of a traditional method in a complex environment is overcome, and high-precision and high-robustness transformer abnormal sound source positioning is realized.

The invention relates to the technical field of bridge detection, and specifically discloses a bridge prestress loss measurement system based on acoustic emission technology, comprising an emission signal acquisition module, a sound source positioning module, an environment monitoring module, a prestress loss measurement module and a data analysis module; the acoustic emission signal acquisition module collects acoustic emission signals in the process of bridge prestress loss; the sound source positioning module is responsible for helping to accurately locate the specific location where the prestress loss occurs; the environment monitoring module collects and detects environmental data of the bridge; the prestress loss measurement module measures the prestress loss of the bridge; and the data analysis module analyzes the environmental data acquired by the sensor together with the acoustic emission signal.

The invention relates to a multivariate microphone array sound source localization method based on GS-RBF and time delay estimation, and belongs to the technical field of sound source localization, and the method comprises the following steps: S1, building a near-field model of a fault sound source, and carrying out the design of a microphone array; s2, calculating a time difference of arrival (TDOA) matrix based on a generalized cross-correlation phase transformation algorithm GCPHAT; s3, constructing a sound source localization model based on an RBF neural network; and S4, optimizing the sound source positioning model based on a grid search algorithm GS, and positioning the sound source by using the optimized model. According to the method, the rapidity and accuracy of fault sound source positioning during unmanned aerial vehicle inspection are ensured, the anti-noise performance and the spatial resolution capability in a complex environment are considered, and an efficient and reliable technical solution is provided for detection of hidden faults of the power transmission line.

The invention discloses a sound source localization method based on multi-frequency separation and Newton optimization deconvolution, relates to the technical field of array acoustic signal processing, and is used for solving the problem that weak sound sources and multiple sound sources are difficult to identify. According to the method, dominant frequency is extracted through multichannel frequency domain analysis, a cross-spectrum matrix is constructed in combination with a near-field propagation model and a guide vector, delay summation beam forming is executed to obtain sound source preliminary distribution, then the distribution is regarded as a convolution result, a maximum likelihood model is introduced, and a two-stage deconvolution strategy of coarse estimation and Newton method fine optimization is adopted to obtain a high-resolution sound source. According to the multi-sound-source positioning method, subgrid-level analysis of sound source positions and amplitudes is achieved, finally, all frequency results are fused, continuous sound source images are smoothly output through a two-dimensional Gaussian kernel, the resolution and real-time performance of multi-sound-source positioning are remarkably improved, and the multi-sound-source positioning method is suitable for high-precision acoustic imaging in a complex sound field.

The invention relates to the technical field of bridge detection, and specifically discloses a bridge prestress loss measurement system based on acoustic emission technology, comprising an emission signal acquisition module, a sound source positioning module, an environment monitoring module, a prestress loss measurement module and a data analysis module; the acoustic emission signal acquisition module collects acoustic emission signals in the process of bridge prestress loss; the sound source positioning module is responsible for helping to accurately locate the specific location where the prestress loss occurs; the environment monitoring module collects and detects environmental data of the bridge; the prestress loss measurement module measures the prestress loss of the bridge; and the data analysis module analyzes the environmental data acquired by the sensor together with the acoustic emission signal.

The invention provides a sound source localization rehabilitation training method, system and device and a medium. The sound source localization rehabilitation training method comprises self-adaptive training and scene simulation training, the self-adaptive training comprises the steps of providing a training task in the period, and the training task in the period comprises the step of selecting training difficulty; according to the training difficulty, sound stimulation is presented in different spatial orientations, and the training difficulty can be adjusted according to a certain logic; the training difficulty of the next period of training is adaptively adjusted according to the correct rate p of one training group of the subject until the preset training amount is reached, and the difficulty setting includes but is not limited to scene setting, analog input information amount and signal-to-noise ratio adjustment. According to the scheme, a hearing-impaired person can perform rehabilitation training in a home environment, the need of manual intervention is reduced, and the training convenience and flexibility are improved. Through adaptive training, on the basis of ensuring scientificity, accuracy and effect of a training plan, the positioning ability of the hearing-impaired person is enhanced, and the integration of the hearing-impaired person into the society is accelerated.