6223 results about "Sound sources" patented technology

The invention relates to a multi-microphone array beam forming signal enhancement method and device, and the method comprises the steps: obtaining a multi-channel time domain signal collected by a multi-microphone array, and carrying out the time-frequency conversion, and obtaining a plurality of time-frequency domain signals; acquiring a spatial distribution vector of the multi-microphone array, and performing deformation compensation processing on the complex number of time-frequency domain signals to obtain sound field consistency signals; performing sound source direction feature extraction on the sound field consistency signal, and performing complex time-space attention beam forming network processing to obtain a beam forming signal; performing beam direction and gain parameter optimization on the beam forming signal through a preset adversarial environment simulator to obtain a dynamic frequency band enhanced signal; and performing inverse time-frequency conversion on the dynamic frequency band enhanced signal, and performing low-delay optimization to obtain a target quality voice output signal. According to the invention, signal deformation compensation can be carried out more accurately.

The invention relates to the technical field of voice processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a voice intention recognition method, device and equipment and a medium, and the method comprises the steps: obtaining a to-be-processed voice signal, carrying out the voice activity detection processing of the voice signal, dividing the voice signal into a plurality of voice segments, analyzing semantic contents of the plurality of voice segments, determining semantic correlation information of each voice segment, analyzing sound source attributes of the plurality of voice segments, determining sound field type information of each voice segment, screening out a target voice segment from the plurality of voice segments according to the semantic correlation information and the sound field type information, and executing intention recognition processing based on the target voice segment to generate an intention recognition result. According to the invention, through a dual analysis mechanism of semantic correlation information and sound field type information, effective screening of voice segments is realized before voice recognition, non-target voice or interference segments are effectively prevented from being sent to an intention recognition model, and the accuracy of a recognition result is improved.

The invention discloses a multi-screen voice interaction system and method applied in an automobile cabin, and relates to the technical field of vehicle-mounted intelligent interaction, and the system comprises a voice collection unit which is used for carrying out sound source positioning and collection, carrying out the noise reduction processing of a collected voice signal, extracting features from the processed voice signal, and generating a voice feature vector; the multi-modal sensing unit is used for collecting behavior information and physiological state data of a driver and passengers through multi-sensor fusion, so as to extract multi-modal information; the man-machine interaction unit is used for carrying out space-time modeling on the multi-modal information to generate a scene state vector; and the cooperative scheduling unit is used for realizing multi-screen intelligent distribution and cooperative control. According to the invention, voice instructions of a driver and passengers can be accurately identified, the safety and experience of the driver are improved, changes in different driving environments are dynamically adapted, the operation efficiency and user experience of a vehicle-mounted system are improved, and the intelligence and adaptability of the system are improved.

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 provides a civil air defense engineering concealed conduit leakage positioning method based on acoustic characteristics, and belongs to the technical field of civil air defense engineering. An acoustic signal acquisition network is constructed by deploying a high-sensitivity hydrophone array, and signals are preprocessed by using a time division multiple access technology and a pulse compression technology; and carrying out time-frequency analysis by applying wavelet transform to extract acoustic features. A time reversal mirror technology is introduced to identify direct propagation and multipath reflection signals, a fluid acoustic coupling propagation equation is constructed to analyze a leakage sound source mechanism, and pipe network topological information and a sound wave speed correction function are combined to compensate a measurement error. Wherein the deep learning pipe network acoustic propagation multi-modal model is fused with a pipeline structure encoder, an acoustic feature extractor and a position prediction decoder, and high-precision leakage positioning and degree evaluation in a complex environment are realized through a three-stage pre-training strategy; the technical problem that it is difficult to accurately locate the position of a leakage point in the complex pipe network environment of civil air defense engineering is solved.

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 provides a classroom behavior analysis method and system, electronic equipment and a storage medium, and relates to the technical field of educational informationization, and the method comprises the steps: collecting videos and audios, carrying out image enhancement, noise filtering and frame segmentation on the videos, carrying out noise reduction, sound source positioning and voice segmentation on the audios, and generating standardized images and voice sequences; an improved MTCNN cascade network is combined with a posture estimation technology, facial key points of students are extracted from videos, class arrival states, head postures and facial micro-expressions are recognized, audios are analyzed through a bidirectional LSTM network, and speaking duration and frequency are obtained; constructing individual behavior indexes according to attendance, head actions and expressions of the students; constructing an interaction index according to the teacher and student speaking data; the individual and interaction indexes are summarized to generate the multi-dimensional classroom behavior portrait, and the relation between the portrait and the teaching effect is analyzed, so that the classroom behavior analysis precision can be improved, the data processing capability can be enhanced, and the multi-dimensional classroom behavior comprehensive evaluation can be realized.

The invention relates to the technical field of nondestructive testing, in particular to a leak detection and partial discharge digital imaging detection method and system based on acousto-optic fusion, and the method comprises the following steps: based on channel microphone array sound wave data in a partial discharge signal suspicious region, extracting a sound wave abnormal section, positioning a sound source, matching image edge features, and synchronously marking; and analyzing the phase change of the multi-frequency signal to judge a sound source concentration area, tracking the moving trend of a disturbance point, and outputting an acousto-optic fusion positioning trend track. According to the method, the partial discharge feature recognition sensitivity is improved through high-frequency peak paragraph screening and dominant frequency recognition, the abnormal region positioning precision is enhanced in combination with image edge extraction and sound source space matching, and acousto-optic synchronous positioning and trend trajectory display are achieved through multi-frequency signal phase analysis and image frame disturbance tracking. Through fusion of frequency domain feature extraction, image recognition, dynamic comparison and other actions, the spatial precision of abnormal source recognition and the multi-source fusion analysis efficiency are improved, and the partial discharge traceability and dynamic monitoring capability are enhanced.

The invention discloses a rescue method and system of a rescue robot for exploration, and relates to the technical field of underground space rescue, and the method comprises the following steps: obtaining multi-path acoustic echo data of a karst cave and motion track data of the robot, and constructing a three-dimensional point cloud model according to the multi-path acoustic echo data and the motion track data of the robot; and identifying unmatched data in the multi-path acoustic echo data and the robot motion trail data, and taking an area where the unmatched data is located as an abnormal area. According to the method, the spatial resolution of signal acquisition is enhanced through the multi-microphone array, robust acoustic fingerprints are extracted in combination with a noise reduction algorithm and short-time Fourier transform, high-confidence human body sound source signals are screened out by using a feature template matching mechanism, accurate extraction and recognition of human body acoustic features in a karst cave complex noise environment are realized, and the accuracy of human body acoustic feature recognition is improved. The problem of misjudgment caused by confusion of sound source features and environmental noise in a traditional method is effectively solved.

The invention relates to the technical field of industrial monitoring, in particular to a fan running state comprehensive monitoring system and a noise and current detection method thereof, and the system comprises a noise data acquisition module, a vibration noise correlation analysis module, a running current acquisition module, an abnormal current detection module, an equipment fault diagnosis module and a performance degradation prediction module. According to the method, the sound data are collected, the frequency and amplitude changes are analyzed, the types of various sound sources are recognized, abnormity caused by mechanical parts is found in the early stage, association analysis of vibration data and sound data is combined, understanding of the physical state of the fan is enhanced, and by monitoring current data and the fan blade rotating speed in real time, the fan blade rotating speed can be accurately detected. Abnormal detection of the current data and evaluation of the current stability are realized, a data basis is provided for fault diagnosis, the operation efficiency and the performance degradation trend of the fan are evaluated in combination with the current data, the service life and the maintenance period of equipment are effectively predicted, and the operation strategy and the maintenance plan of the fan are optimized.

To provide an information processing system, a control method for the information processing system, and a program that allow a user to distinguish a sound from a specific sound source while leaving the sounds from each sound source as they are, even when a plurality of sound sources are present in a direction of interest that is of interest to the user.SOLUTION: An information processing system 1000 comprises: display means (a display unit 107) capable of displaying an image of a mixed reality space; playback means (an audio output unit 105) capable of playing back sounds from a plurality of sound sources to a user; vibration applying means (a vibration unit 117) for applying vibration to the user; control means (a control unit 111) for controlling displaying a virtual object that can be linked to each sound source on the display means; and selection means for selecting the virtual object (an object selection instruction unit 116). The vibration applying means applies vibration to the user in accordance with the intensity of the sound from the sound source associated with the virtual object if the virtual object is selected.SELECTED DRAWING: Figure 2

The invention provides a real-time video translation and audio and picture synchronization method and system based on a multi-modal large model, and relates to the technical field of video translations, and the method comprises the steps: obtaining a source video; extracting the source video based on the multi-modal large model to obtain a multi-modal feature; fusing the multi-modal features through a cross-modal attention mechanism to generate a context semantic vector; translating into a target language text in real time based on the context semantic vector, and processing the translated language text based on the multi-modal features to obtain a translated language sound source; and performing mouth shape adjustment on the source video based on the translation language sound source, and merging the translation language sound source and the mouth shape animation video to obtain a real-time translation video with synchronous sound and picture. According to the method, the limitation of traditional single-modal translation is broken through, and the semantic accuracy of translation is remarkably improved by dynamically aligning the context information through the multi-modal features in combination with a cross-modal attention mechanism.

This disclosure is related to systems and methods for rendering audio for a mixed reality environment. Methods according to embodiments of this disclosure include receiving an input audio signal, via a wearable device in communication with a mixed reality environment, the input audio signal corresponding to a sound source originating from a real environment. In some embodiments, the system can determine one or more acoustic properties associated with the mixed reality environment. In some embodiments, the system can determine a signal modification parameter based on the one or more acoustic properties associated with the mixed reality environment. In some embodiments, the system can apply the signal modification parameter to the input audio signal to determine a second audio signal. The system can present the second audio signal to the user.

The invention relates to a pickup control method and device based on sound position recognition, and the method comprises the steps: obtaining input data of a plurality of microphones, determining sound source feature data based on the input data of the plurality of microphones, and modulating the output of a target sound source and / or a non-target sound source based on the sound source feature data. According to the embodiment of the invention, the target sound and the non-target sound can be automatically identified and separated according to the sound source feature data, the output of the target sound is enhanced, the communication quality is improved, the non-target sound is effectively inhibited, the interference of background noise is reduced, and the responsiveness and efficiency of a sound processing system are improved.

The invention relates to the field of environmental noise monitoring, and discloses a noise monitoring point position automatic determination method fused with a noise map. The method comprises the following steps: acquiring a base map and sound source data of a target area, layering the base map according to a preset interval in a vertical direction, and carrying out gridding processing to obtain a three-dimensional grid; performing spatial analysis on the sound source data to obtain a sound source intensity matrix, and mapping the sound source intensity matrix to a three-dimensional grid to obtain a three-dimensional noise map; the base map comprises building structure data; performing three-dimensional path exploration on the three-dimensional noise map based on an ant colony algorithm to obtain a noise hotspot path and a pheromone concentration distribution diagram; and carrying out site selection analysis based on a genetic algorithm on the noise hotspot path and the pheromone concentration distribution diagram to obtain a Pareto optimal monitoring site set, the Pareto optimal monitoring site set being used for indicating site selection of the noise automatic monitoring site. By adopting the method, accurate and efficient automatic noise monitoring site selection in a complex urban environment can be realized, and the urban noise monitoring and treatment level is improved.

The invention provides an audio processing device and method, an augmented reality device, equipment and a storage medium, and belongs to the technical field of virtual reality display and sensing. The audio processing device comprises a positioning device which is configured to track and position a user in a real space in real time, determine positioning information of the user and send the positioning information to a processor; the processor is configured to determine target positioning information of the user based on pre-established spatial information and the real-time positioning information of the user; determining association relationship information between the user and a virtual sound source in a virtual space based on pre-established space information and real-time target positioning information of the user; obtaining human body parameters of the user, and determining an audio filtering result matched with the user based on the association relationship information, the human body parameters and the audio frequency of the virtual sound source; and reconstructing the audio to be played based on the audio configured for the virtual sound source in advance and the audio filtering result.

The invention provides a transformer partial discharge ultrasonic propagation simulation model construction method and system, and the method comprises the steps: defining a geometric model of a transformer, which is a three-dimensional model of an oil-filled transformer oil tank with a winding and an iron core; setting a sound field module and defining a sound source, and placing the sound source as a partial discharge source in the geometric model of the transformer; a hard boundary condition is applied to the surface of the geometric model tank of the transformer, reflection of the geometric model tank to sound pressure waves is obtained, and under the hard boundary condition, the normal derivative of pressure is zero at the boundary; performing grid division on the geometric model body of the whole transformer by adopting tetrahedral grid units; and after solving, further processing to obtain model parameters, wherein the model is used for displaying the distribution of the ultrasonic sound pressure in the transformer.

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.

Approaches presented herein provide for identification of sound from a sound source relative to an array of microphones of a potentially unknown configuration using, in part, differences in the audio signals received by the microphones. In at least one embodiment, audio signals are captured using an array of microphones and audio features are extracted from those signals. The audio features can be processed using a first neural network to generate a feature vector representing a spatial location of an audio source with respect to the plurality of microphones, where the spatial location is inferred based on audio differences and independent of an availability of information indicating a physical configuration of the plurality of microphones. The feature vector can be provided to a task-specific model to perform at least one audio-related task based in part on the spatial location.

An underwater acoustic sensor network node positioning error correction method combined with acoustic velocity inversion, which method relates to the technical field of acoustic velocity inversion. The method comprises: deploying an acoustic source point and underwater acoustic sensor nodes which are at fixed positions, the underwater acoustic sensor nodes collecting acoustic velocity data sent by means of the acoustic source point, and by means of acoustic velocity data at different underwater acoustic sensor nodes, using an acoustic velocity field inversion algorithm to construct an acoustic velocity field model; collecting real-time acoustic velocity data at the different underwater acoustic sensor nodes, and obtaining acoustic velocity characteristic information at the different underwater acoustic sensor nodes and water characteristic information at the different underwater acoustic sensor nodes; performing comprehensive analysis on the acoustic velocity characteristic information and the water characteristic information, and evaluating the positioning accuracy of the acoustic velocity field model in respect of the underwater acoustic sensor nodes; and comparing quantized positioning accuracy results for the underwater acoustic sensor nodes with a preset threshold, and when there is a relatively large error in the positioning accuracy, generating an error signal. The method is conducive to improving the precision of underwater acoustic wave propagation and the accuracy of underwater acoustic sensor network node positioning, and evaluating the positioning accuracy of an acoustic velocity field model in respect of underwater acoustic sensor nodes.

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 discloses an offshore wind turbine blade online monitoring method based on voiceprint recognition, which belongs to the field of wind turbine blade acoustic signal analysis, and comprises the following steps: acquiring wind turbine voiceprint data, weather condition data and wind turbine generating capacity data, and constructing a signal processing model; the collected voiceprint signals are subjected to denoising, amplification and feature extraction through a signal amplifier, a filter and a feature extraction unit, a deep learning model is constructed, voiceprint signal parameters, weather data and wind turbine generating capacity are imported into the deep learning model to evaluate future wind turbine blade cracks, a crack evaluation model is constructed, and the wind turbine blade cracks are evaluated. And importing the crack sound source position, the crack depth, the crack length and the estimated damage score into a crack evaluation model to evaluate the crack degree of the fan blade under the future weather condition, grading the fan cracks according to the crack damage score, and giving an alarm. The early warning of the future crack condition of the fan blade is realized by combining the voiceprint signal of the offshore fan blade, the weather condition and the generating capacity.

The invention provides a multi-mode sensing intelligent microphone array signal processing method and system, and belongs to the technical field of signal processing, and the method comprises the steps: obtaining a visual signal through employing a multi-directional visual sensor, and obtaining a sound signal through employing a microphone array; extracting visual features and acoustic features; constructing an audio-visual topological feature space, mapping visual features and acoustic features to the space, and establishing a sound source probability distribution model; processing the sound signal by adopting a multi-dimensional discriminant adversarial generative network, and separating out a target voice signal; the acoustic environment state is evaluated in real time, and processing parameters are dynamically adjusted; quality evaluation is carried out on the separated multiple paths of target voice signals, the voice signal with the highest quality is selected as output, audio-visual multi-mode information is deeply fused and cooperatively processed, and a topology enhanced adversarial generative network architecture and an environment self-adaptive mechanism are combined, so that the voice separation effect in a complex environment is remarkably improved; and more than 85% of speech intelligibility can still be kept in a scene that six persons speak simultaneously.

An autonomous mobile robot control method and apparatus, a device and a readable storage medium. An autonomous mobile robot determines a sound source direction according to a voice signal from a user, and determines moving objects around the autonomous mobile robot itself. The autonomous mobile robot determines, from the moving objects, a target object located in the sound source direction, determines a working area according to the target object, and moves to the working area, and executes a task.

The invention discloses an automatic conference summary generation method based on multi-source heterogeneous information fusion. The method comprises the following steps: extracting facial features of a speaker by using a face recognition technology and recognizing a face identity of the speaker; acquiring an audio signal through a microphone array, and recognizing a voiceprint identity of a speaker by using a voiceprint recognition technology; in combination with video and audio information, through a multi-source heterogeneous information fusion technology, alignment of video and audio data is carried out in time; through a sound source positioning technology, a voiceprint identity and a face identity are matched and aligned in space, and the identity of a speaker is accurately positioned and recognized; the spokesman and the speaking content are calibrated and separated, and it is ensured that the identity of the spokesman is accurately matched with the speaking content; and generating a spokesman abstract and a conference summary according to the calibrated speech content by using natural language processing and a deep learning model. The method is suitable for various conference scenes, the speaking content of different spokesmen can be recognized, the spokesman abstract is generated, and the efficiency and accuracy of conference summary generation are improved.

A sound system for a vehicle is configured to implement a soundscape that matches an environment around the vehicle. The sound system includes a noise meter that measures environmental noise generated around the vehicle, a vibration meter that measures vibration generated by the vehicle, and a processor connected with the noise meter and the vibration meter. The processor determines a noise type by analyzing features of the vibration and the environmental noise respectively measured by the vibration meter and the noise meter, selects a sound source matched with the noise type among sound sources previously stored in a database, and plays and outputs the selected sound source.

The invention discloses a transcranial ultrasonic multi-frequency acoustical holographic lens design method and system. The method comprises the following steps: setting a plurality of different focus points; using a k-wave toolbox in Matlab, respectively setting a plurality of different focus points as sound sources by using a time reversal method, respectively adopting a plurality of driving frequencies to carry out simulation experiments, capturing the phase of the plane where the ultrasonic transducer is placed, and obtaining a plurality of acoustic holograms; according to the method, effective information of a plurality of acoustical holograms is extracted and integrated into a multi-frequency acoustical hologram, and after a transcranial multi-frequency acoustical hologram is obtained, phase information of the multi-frequency acoustical hologram is extracted and converted into the height of each pixel of the acoustical hologram lens. Multi-frequency transcranial focusing based on the multi-frequency acoustical holographic lens is realized by extracting effective information of a plurality of acoustical holographic lenses which are focused at different positions and have different working frequencies and integrating the effective information into one multi-frequency acoustical holographic lens, and corresponding focuses are reconstructed on a focusing plane according to different driving frequencies, so that flexibility in transcranial focusing is realized, and the focusing precision is improved. The application potential is improved.

The embodiment of the invention provides an auditory attention decoding method and device based on a multi-sound-source scene, equipment and a medium, and belongs to the technical field of electroencephalogram signals. The method comprises the steps of obtaining an initial electroencephalogram signal responded by an initial user under first sound stimulation, a multi-sound-source voice signal of the first sound stimulation and a sound source label of the multi-sound-source voice signal; preprocessing the initial electroencephalogram signal and the multi-sound-source voice signal to obtain a to-be-processed electroencephalogram signal of the initial electroencephalogram signal and a to-be-processed voice signal of the multi-sound-source voice signal; training the initial auditory attention decoding model according to the to-be-processed electroencephalogram signal, the to-be-processed voice signal and the sound source label to obtain a target auditory attention decoding model; obtaining a target electroencephalogram signal responded by the target user under the second sound stimulation and a target voice signal of the second sound stimulation; and performing auditory attention decoding on the target electroencephalogram signal and the target voice signal by using a target auditory attention decoding model to obtain an attention target.

The invention discloses a lamp strip module atmosphere creating method based on scene induction control, and relates to the technical field of intelligent illumination control, and the method comprises the following steps: S1, collecting an original audio signal of a target space, extracting amplitude fluctuation, a spectrum structure and energy mutation features, recording the starting time and duration of a sound event, and constructing a sound source time sequence; according to the invention, through sound source time sequence modeling, abnormal behavior identification and environment trend analysis, in combination with a multi-dimensional consistency judgment mechanism, accurate response to an abnormal sound source is realized, safe transition is carried out through neutral color temperature lighting effect, and then light effect smooth regression is realized through dynamic recovery evaluation and a slow changing algorithm, so that the accuracy of the abnormal sound source is improved. An intelligent light control system with real-time perception, context understanding and closed-loop regulation and control capabilities is constructed, immersion experience and safety are improved, and the intelligent light control system has remarkable practical value and technical innovation.