575 results about "Sound recognition" patented technology

The invention relates to the technical field of sound anomaly detection, in particular to a sound anomaly detection method and device based on a Transform model, equipment and a medium, and the method comprises the steps: collecting a sound signal during the operation of the equipment through a data collection interface, and obtaining an original sound signal; resampling is carried out on the collected sound signals, and normalization processing is carried out on the resampled data; mel-frequency cepstrum coefficient features are extracted from the sound signals after normalization processing, and the sound signals after normalization processing are input into a pre-training module to output high-dimensional features including time sequence and semantic information; splicing the Mel-frequency cepstrum coefficient features with the high-dimensional features to form a comprehensive feature vector; inputting the comprehensive feature vector into a support vector machine model, and performing abnormal sound recognition through a trained classification hyperplane; and detected abnormal information is fed back to the user in real time. Multi-feature fusion enables the model to identify abnormal sound more accurately.

The invention discloses a method for monitoring abnormal working conditions of a draining pump and an outlet pipeline thereof based on sound recognition, and the method comprises the steps: collecting voiceprint recognition related data, including relay operation signals, infrared temperature measurement image data, humidity data and audio data; the collected voiceprint recognition related data is transmitted to a cloud data platform of a server through a data line, and feature value extraction is performed on the audio data through a voiceprint feature value data edge calculation gateway of the cloud data platform; performing voiceprint data processing on the audio data through a voiceprint recognition algorithm; carrying out comprehensive fault judgment; the method solves the problems of low efficiency and insufficient accuracy caused by manual monitoring and recognition of the abnormality of the draining pump in the prior art, and has the characteristics of being capable of performing comprehensive fault judgment through combination of multi-modal data and voiceprint scoring, and greatly improving the recognition rate and accuracy of abnormality or fault judgment of the draining pump.

The invention provides an automobile wire harness performance parameter test system, and relates to the technical field of automobile wire harnesses, the automobile wire harness performance parameter test system comprises a test assembly and a control module, the test assembly comprises a simulation test group and a mute test box, and the control module comprises an acquisition module, an analysis module, a judgment model unit and a visual feedback platform; a simulation system is constructed in the simulation test group, and when the automobile wire harness is installed in the simulation system for operation, operation state parameters of an automobile under different road conditions and environment conditions are simulated; the simulation system is constructed to simulate the running state parameters of the automobile under different road conditions and environment conditions, the change relevance between the running state parameters and the current parameters of the automobile wire harness is analyzed, the reference variable is obtained, the first performance of the automobile wire harness is comprehensively judged, the actual running requirements of the automobile wire harness are met, and the reliability of the automobile wire harness is improved. The sound of the automobile wire harness is synchronously recognized and collected through the mute test box, the abnormal type of the wire harness represented by the sound is recognized, the second performance of the automobile wire harness is judged, and the method is more comprehensive.

A multi-dimensional hydropower station control panel cabinet working condition abnormity monitoring method based on sound recognition is characterized in that a sound pickup, a mutual inductor, an infrared image monitor, a switch node sensor and the like are installed at different positions of a control panel cabinet to acquire multi-dimensional data including voiceprint, voltage, current, temperature, images and switch displacement signals; the voiceprint signals are subjected to pre-emphasis, framing, FFT transformation, Mel frequency filter bank application, MFCC feature extraction and the like, and feature vectors are extracted by using UBM, GMM and MAP algorithms. A PLDA algorithm is adopted for data analysis and fault judgment, multi-dimensional data comprehensive scoring is fused, the fault type is determined, alarm information and processing measures are output, and the system has the advantages of comprehensive multi-dimensional data acquisition, advanced signal processing technology, scientific and accurate fault judgment, high practicability and the like.

The invention discloses an automobile environment sound enhancement method and device, electronic equipment and a storage medium, which are used for solving the technical problems of low automobile external environment sound recognition precision and poor environment adaptability in related technologies. The method comprises the following steps: acquiring audio data outside a vehicle and video data outside the vehicle; scene recognition is carried out according to the vehicle exterior audio data and the vehicle exterior video data, and the current environment sound type and the current driving environment are obtained; extracting visual information from the video data outside the vehicle, and extracting audio information from the audio data outside the vehicle; carrying out multi-modal data fusion based on comprehensive confidence in combination with the visual information and the audio information to obtain an effective scene judgment result; and based on the current driving environment, the current environment sound type and the effective scene judgment result, the external environment sound is enhanced through a sound unvarnished transmission enhancement strategy. Therefore, environmental sound recognition accuracy can be improved based on multi-modal data fusion, environmental sound enhancement is realized in combination with a sound unvarnished transmission enhancement strategy, and environmental adaptability is improved.

The invention discloses a sound acquisition and processing system based on cooperation of multiple microphone arrays. The system comprises a sound acquisition module, a multi-channel signal preprocessing module, a sound signal feature extraction module, an abnormal sound recognition module, a sound source positioning module and an alarm module. A multi-channel mixed data signal is collected through a circularly-arranged multi-microphone array formed by a plurality of microphones, after echo cancellation, wave beam domain noise reduction and multi-sound-source separation, a single-sound-source feature vector is extracted, according to the single-sound-source feature vector, abnormal sound including explosion, screaming or glass breakage is recognized through a BiLSTM and an attention mechanism model, and the abnormal sound is recognized through an attention mechanism model. And the GCC-PHAT and MDS-MUSIC algorithms are combined to position abnormal sound production, and alarm information is generated. According to the invention, accurate identification, positioning and alarm of the abnormal sound can be realized, and the real-time performance, the accuracy and the multi-target processing capability of abnormal sound monitoring in a complex environment can be obviously improved.

The invention discloses a double-branch sound-vibration fusion event recognition and positioning method based on DAS and AI, and the method comprises the steps: synchronously collecting sound and vibration data, and constructing a sound recognition branch and a vibration positioning branch; the sound branches extract multi-scale joint features through time-frequency domain feature fusion, weights of CNN and BiLSTM are dynamically adjusted to realize adaptive fusion, and event types and confidence coefficients are output; and the vibration branch calculates an initial coordinate by using a four-point space-time weighted optimization method, performs position fine tuning in combination with an ASTCN network, and outputs a final event position. Performing probability distribution verification by fusing double branch results and adopting a dynamic likelihood ratio evaluation mechanism, and outputting a final judgment result; intelligent event classification is achieved through the voice recognition branch, high-precision event space-time positioning is achieved through the vibration positioning branch, intelligent cooperation and system optimization of multi-modal data are achieved through the fusion and verification module, and the bottlenecks of the traditional technology in the aspects of recognition precision, positioning errors and system robustness are effectively broken through.

The invention discloses a photovoltaic station monitoring method and device based on multi-modal fusion, a storage medium and electronic equipment. The method comprises the following steps: collecting multi-modal monitoring data of a photovoltaic station, wherein the multi-modal monitoring data comprises image data, audio signals and sensor data; inputting the image data into a trained target detection model for target detection to obtain an image recognition result; inputting the audio signal into a recursive convolutional neural network for feature extraction to obtain a sound recognition result; inputting the sensor data into a mathematical statistical analysis model for classification detection, and outputting an abnormal equipment signal; and fusing the image recognition result, the sound recognition result and the abnormal equipment signal to obtain a fusion result, and when the fusion result meets a preset early warning rule, sending out an early warning signal. According to the invention, the potential safety hazard of the photovoltaic station can be comprehensively and accurately identified.

A system for presenting audio content to a user. The system comprises one or more microphones coupled to a frame of a headset. The one or more microphones capture sound from a local area. The system further comprises an audio controller integrated into the headset and communicatively coupled to an in-ear device worn by a user. The audio controller identifies one or more sound sources in the local area based on the captured sound. The audio controller further determines a target sound source of the one or more sound sources and determines one or more filters to apply to a sound signal associated with the target sound source in the captured sound. The audio controller further generates an augmented sound signal by applying the one or more filters to the sound signal and provides the augmented sound signal to the in-ear device for presentation to a user.

The invention provides a laying hen voice recognition method and system fusing acoustic features and deep learning features. The method comprises the steps of obtaining a to-be-recognized original audio signal and a voice recognition model; wherein the voice recognition model comprises a feature extraction network, a feature fusion network and a classification recognition network; performing feature extraction on the original audio signal by using the feature extraction network to obtain a spectrogram feature, a Mel-frequency cepstrum coefficient feature and a deep speech feature; the feature fusion network performs feature fusion on the spectrogram features, the Mel-frequency cepstrum coefficient features and the deep speech features by using a collaborative attention mechanism or a multi-head attention mechanism to obtain fused features; and inputting the fused features into a classification recognition network to obtain a voice recognition result. According to the method, the advantages of various characteristics can be fully utilized, and the sound signals are described and analyzed from multiple angles, so that the voiceprint of the laying hen is more accurately recognized, and the voiceprint recognition accuracy of the laying hen is remarkably improved.

The invention discloses a cough sound recognition method for health monitoring, and belongs to the technical field of health monitoring, auxiliary diagnosis and treatment and medical data processing, and the method comprises the steps: (1) sound collection and noise reduction processing are carried out, and clear external audio is obtained; (2) cough sound segmentation: only retaining audio clips which may be cough in the audio; (3) cough sound feature extraction: extracting audio fusion features including MFCC features and first-order and second-order differences of the MFCC features; (4) cough sound confirmation: further screening out all cough sounds from the segmented audio clips; (5) monitoring object identification: judging whether each cough segment is a monitoring object or not; (6) identifying the cough sound, and judging the possible disease of the cough sound of the monitored object; according to the method, the extraction and recognition accuracy of the cough sound of the monitoring personnel can be remarkably improved.

The described aspects and implementations enable privacy-respecting detection, separation, and localization of sounds in vehicle environments. The techniques include obtaining, using audio detector(s) of a vehicle, a sound recording that includes a plurality of elemental sounds (ESs) in a driving environment of the vehicle, and processing, using a sound separation model, the sound recording to separate individual ESs of the plurality of ESs. The techniques further include identifying a content of individual ESs and causing a driving path of the vehicle to be modified in view of the identified content of the individual ESs. Further techniques include rendering speech imperceptibly by redacting temporal portions of the speech, using sound recognition models to identify and discard recordings of speech, and driving at speeds that exceed threshold speeds at which speech becomes imperceptible from noise masking.

The invention relates to the technical field of transformer substation high-voltage equipment detection, and discloses a transformer substation high-voltage equipment abnormal sound recognition and detection device which comprises a voiceprint recognition unit, an ozone gas detection unit, a partial discharge sensing unit, a data processing and analysis unit and an early warning unit. The voiceprint recognition unit collects the operation sound of the equipment in real time; the ozone gas detection unit detects the ozone concentration in the air; the partial discharge sensing unit collects a partial discharge signal of the equipment; and the data processing and analyzing unit is used for processing and analyzing based on the standby operation sound data, the ozone concentration data and the partial discharge signal of the equipment, and carrying out accurate identification and early warning on the abnormal state of the equipment based on an analysis result. The device has the advantages of multi-dimensional monitoring, real-time performance, visualization, intelligence and the like, and can realize effective monitoring and fault early warning of the state of the high-voltage equipment; actual tests show that the device can significantly improve the operation efficiency and safety of the power grid and reduce the operation and maintenance cost.

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 a vehicle-mounted voice identification and positioning intelligent early warning system and method, and relates to the technical field of vehicle-mounted intelligent early warning. The system comprises a sound detector module which collects an audio signal of a surrounding environment in real time, and a sound recognition module which analyzes the audio signal through a deep learning model and recognizes a sound source type. And the calibration module performs space-time calibration to ensure data synchronization and alignment of the sensors. The positioning module combines time difference of arrival (TDOA) and a sound intensity attenuation model, and optimizes sound source positioning through particle swarm optimization (PSO) and extended Kalman filter (EKF) technologies. The risk assessment module assesses the collision risk based on the sound source position, the shielding state and the object type, gives an alarm to a driver in time through the early warning module, and automatically triggers a deceleration instruction. And the self-learning module dynamically adjusts the early warning sensitivity and the noise compensation coefficient through a federated learning framework updating model. The environmental adaptability of the vehicle-mounted early warning system is improved, and the vehicle-mounted early warning system has remarkable safety improvement and wide application prospects.

The invention relates to the technical field of sound recognition, in particular to an optical fiber sensing voiceprint feature analysis model construction method of a composite neural network, and the method comprises the steps: collecting a sound signal through distributed optical fiber sensing, setting a sound frequency amplitude threshold value, and extracting a sound signal of an abnormal interval; carrying out noise reduction processing on the collected signals and extracting voiceprint features; constructing an unsupervised model to calculate an outlier score value, and combining a threshold value to judge whether the signal is an abnormal signal; carrying out principal component analysis on the abnormal signal, and carrying out feature compression and mapping; a composite neural network model is trained based on the extracted features, and multi-class voiceprints are recognized; and an uncertainty evaluation mechanism is introduced, a classification result is dynamically adjusted according to confidence, low-confidence data is marked as unidentified and collected and classified again, and self-learning and iterative optimization of voiceprint recognition are realized. The method provided by the invention has high precision, high robustness and strong generalization ability, and is suitable for optical fiber voiceprint event recognition in a complex environment.

The invention discloses a power distribution line bird repelling method and system based on Internet of Things edge calculation, and relates to the technical field of power distribution network safety. The system comprises: a bird condition acquisition module configured to obtain a bird audio and a bird image to be detected; a model identification module configured to identify the bird audio by using a bird sound identification model and identify the bird image by using a bird image identification model; the controller module is configured to integrate the bird audio recognition result and the bird image recognition result to obtain the type of the bird; the intelligent decision-making module is configured to generate a bird repelling strategy according to the types of the birds; and the bird repelling module is configured to repel birds according to the bird repelling strategy. According to the invention, birds are identified by utilizing edge calculation, so that different bird repelling modes are adopted for different birds, and intelligent and effective modern bird repelling is realized.

The invention discloses an audio coal gangue identification method. The method comprises the following steps: S1, collecting audio signal data in a coal caving process of a related field of a fully-mechanized face; s2, preprocessing the audio signal data to obtain the audio signal data after noise reduction; s3, performing data calibration on the audio signal features; s4, constructing an audio coal gangue recognition algorithm model; and S5, outputting a coal and gangue recognition result by using the audio coal and gangue recognition algorithm model. According to the invention, a novel audio identification system mainly based on the audio acquisition sensor is utilized, automation of top coal caving of the fully-mechanized caving face is realized through voice identification of coal caving, gangue caving and non-coal caving of the fully-mechanized caving face, and the system has the characteristics of high-precision coal caving and gangue identification and improvement of coal caving efficiency.

The invention discloses a pressure reducing valve multi-mode working condition abnormity monitoring method based on voice recognition. The method comprises the steps that pressure data, flow data, audio data, position switch switching value data, vibration analog quantity data and image data are collected through a comprehensive data collection module; voiceprint recognition related data acquired by the comprehensive data acquisition module is transmitted to a cloud server platform through a network transmission module; identifying and scoring the audio data through a voiceprint scoring module; the fault comprehensive judgment module is used for carrying out fault judgment, the problems that in the prior art, a means for carrying out comprehensive judgment on multiple types of defects is lacked, manual on-site checking is relied on, and the efficiency and accuracy of fault recognition are low are solved, and the advantages that comprehensive fault judgment can be carried out through combination of multi-modal data and voiceprint scoring, and the efficiency and accuracy of fault recognition are improved are achieved. And the recognition rate and the accuracy rate of working condition abnormity or fault judgment of the pressure reducing valve equipment are greatly improved.

The invention discloses a newborn cry recognition system based on machine learning, which belongs to the technical field of intelligent newborn nursing and comprises a data acquisition module, a feature extraction module, a feature selection module, a newborn cry recognition module and a recognition result feedback module. According to the method, the improved badger optimization algorithm is adopted to perform feature selection on the cry audio feature set, key features beneficial to distinguishing different cry categories are automatically selected, manual intervention errors are avoided, feature selection can be better and flexibly adjusted according to individual differences, and it is ensured that the system has high recognition accuracy on different newborn cry; according to the method, the mixed kernel function support vector machine model combined with the K nearest neighbor algorithm is adopted to carry out newborn cry recognition, local similarity improvement between samples is captured, boundaries between different cry categories are accurately divided, and overlapping between the categories is avoided, so that different types of cry are recognized more accurately and efficiently, and the overall usability of cry recognition is improved.

The invention discloses a piano partner training device based on an AI technology, which comprises an AI intelligent processing system, the AI intelligent processing system comprises a sound recognition system, a fingering collection system and a dynamic collection system, the sound recognition system is electrically connected with a high-quality microphone mounted on a piano, and the high-quality microphone is electrically connected with the dynamic collection system. The fingering collection system is electrically connected with a plurality of groups of action collection sensors which are mounted on piano keys and respectively correspond to the keys, and the dynamic collection system is electrically connected with a high-resolution dynamic collection camera mounted on the piano; the AI intelligent processing system further comprises a personalized learning module, a diversified practice module, an interactive feedback and guidance module and a cloud updating and learning module. The invention relates to the technical field of musical instrument training, in particular to a piano partner training device based on the AI technology, which can provide powerful computing power and learning algorithms, provide personalized and real-time feedback and guidance for piano learners and further improve the training effect of the piano learners.

The invention discloses an accident distributing valve multi-mode working condition abnormity monitoring method based on voice recognition. The method comprises the steps that liquid level data, audio data, vibration analog quantity data, temperature measurement image data, active power data and guide vane opening degree data are collected through a comprehensive data collection module; voiceprint recognition related data acquired by the comprehensive data acquisition module is transmitted to a cloud server platform through a network transmission module; identifying and scoring the audio data through a voiceprint scoring module; the fault comprehensive judgment module is used for carrying out fault judgment, the problems that in the prior art, remote monitoring of the accident distributing valve cannot be carried out, fault detection depends on manual on-site checking, and the efficiency and accuracy of fault recognition are low are solved, deep learning is carried out on all data parameters, and fault judgment process logic design is carried out. The fault comprehensive monitoring of the accident distributing valve is realized, and the recognition rate and the accuracy of the abnormal working condition or fault judgment of the accident distributing valve equipment are greatly improved.

The invention belongs to the technical field of traditional Chinese medicine and software, and particularly relates to a traditional Chinese medicine syndrome differentiation aid decision-making method based on trans-modal attention Transform. The invention provides traditional Chinese medicine syndrome differentiation based on cross-modal attention Transform, and aims to explore a more scientific and systematic traditional Chinese medicine syndrome differentiation method by integrating multi-modal data such as visual sense, auditory sense, language, pulse condition and the like. Specifically, information such as complexion and tongue coating is obtained by inspection diagnosis through an image processing technology; the auscultation and diagnosis obtains sound information such as respiration and cough through a sound recognition technology; the inquiry analyzes the chief complaint and symptom description of the patient through a natural language processing technology; pulse condition data are collected through the sensor technology in the clinics. A multi-modal hierarchical dialectical logic framework is constructed, an exclusive attention mechanism is designed for each dialectical method, the overall view and dialectical thinking of traditional Chinese medicine are fully reflected, and a more reasonable dialectical result is generated.

The invention discloses a bird sound recognition method based on multi-model integration. The method comprises the following steps: (1) obtaining a bird sound signal and preprocessing data; (2) extracting key features from the bird sound data by combining Mel-frequency cepstrum coefficient, short-time Fourier transform and decibel conversion feature extraction methods; (3) a plurality of improved pre-training convolutional neural network models are used for training; each convolutional neural network model has different structures or parameter settings so as to capture different features of bird sounds; (4) designing an integration module by using an exponential smoothing method and a weighted smoothing method, and integrating a plurality of trained convolutional neural network models; output results of all the models are fused; (5) carrying out comprehensive performance evaluation on the integrated model; according to the method, the overall accuracy of bird sound recognition is remarkably improved.

The invention relates to a motor abnormal sound identification method and device, electronic equipment and a vehicle, and relates to the technical field of data processing, and the method comprises the steps: firstly obtaining the rotating speed information and sound time domain data of a motor; determining a sampling frequency required for identifying abnormal sound corresponding to the rotating speed information; carrying out downsampling on the sound time domain data according to the sampling frequency; analyzing sound energy corresponding to the rotating speed information based on the downsampled sound time domain data; and identifying whether the motor has abnormal sound or not by comparing the sound energy with a preset energy threshold value. According to the technical scheme, the calculation process of motor abnormal sound recognition can be simplified, the calculation amount is reduced, the recognition cost of the motor abnormal sound is reduced, and the motor abnormal sound of a fixed order is recognized.

The invention discloses a monitoring and traceability system for agricultural product supply chain transportation, and particularly relates to the field of supply chain transportation monitoring and traceability, which comprises the following steps: carrying out damage resistance quantitative evaluation of sliding, collision and extrusion on each agricultural product category in a supply chain, training a sound recognition model based on marked audio signal time-frequency domain features, collecting cargo information and audio data of each link segment in the transportation line, and uploading the cargo information and the audio data to a monitoring center; classifying and recognizing the audio data through a voice recognition model, and calculating a damage assessment score of each link segment in combination with a damage resistance quantitative index; and carrying out visual labeling on a damage evaluation result, sending an abnormal signal to a corresponding link segment, adjusting the transportation density to reduce the damage risk, realizing accurate traceability of the transportation link with abnormal loss, and ensuring that the damage degree of goods in transportation is controllable.

The invention relates to the technical field of machine room monitoring, in particular to an Internet of Things monitoring method for unattended machine room monitoring, comprising an environment monitoring module, a comprehensive gateway, a radar module, a sound collection module and a machine room camera group, the environment monitoring module is used for monitoring machine room environment data; the sound collection module is used for collecting sound data in the machine room in different time periods and transmitting the sound data to the sound analysis module; the machine room camera group is used for acquiring video data in a machine room and monitoring intruders; the radar module is used for multi-target tracking and positioning; the comprehensive gateway comprises a video analysis module and a sound analysis module, and is used for generating an alarm signal according to the machine room environment data, the sound data and a video analysis result. According to the method, video monitoring, millimeter wave radar detection and sound recognition technologies are comprehensively applied, comprehensive monitoring of the internal environment of the machine room is achieved, the detection precision of abnormal events and the accuracy of fault diagnosis are improved, and meanwhile the visual effect of environment monitoring is enhanced.

The invention provides a method for tracking a speaker based on a panoramic camera and a close-up camera. The method comprises the steps that the panoramic camera shoots all people into a panoramic picture; based on a sound source of a speaker and / or analyzing facial actions of all people in the panoramic picture, positioning face position information of the speaker in the panoramic picture; based on the position relation between the panoramic camera and the close-up camera and the face position information of the speaker, the angle and the focal length of the close-up camera are determined, and close-up shooting is carried out on the speaker; the face position of the speaker in the close-up picture is analyzed, and the close-up camera is finely adjusted, so that the speaker is centered in the close-up picture. The position of the speaker is positioned by combining face detection and voice recognition technologies, and the close-up camera picture is triggered to track the close-up picture of the speaker, so that the conference control operation is avoided, and the intelligence of the conference terminal is remarkably improved.

The invention belongs to the technical field of biodiversity observation, and relates to an animal species recognition method based on a microphone array and a sound recognition model, which comprises the following steps of: 1) acquiring three-dimensional space environment sound in real time by using the microphone array; 2) processing the collected three-dimensional space environment sound to obtain a sound signal with improved quality; and 3) based on the quality-improved sound signal, an edge-arranged convolutional attention neural network sound recognition model is adopted to carry out animal species recognition. Accurate recognition of animal sound in a complex acoustic environment is realized, so that animal species categories can be effectively detected and recognized.

The invention discloses a baby cry recognition method for white noise equipment, and relates to the field of audio processing and intelligent acoustic recognition, and the method comprises the steps: collecting a pure reference signal, a first-path signal and a second-path signal, carrying out the synchronization and preprocessing, and obtaining a mixed audio signal based on the first-path signal; a residual signal is calculated through an adaptive echo cancellation algorithm; calculating an acoustic masking parameter based on the residual signal, the pure reference signal, and the ambient noise estimate; constructing a three-level recognition processing path including lightweight feature detection, registration voiceprint comparison and multi-modal information fusion; based on the numerical range of the acoustic masking parameter, selecting an identification processing path, and determining a crying event of the target infant from the residual signal; and triggering a corresponding grading alarm based on a crying event confirmation result. A three-level identification path is scheduled through acoustic masking parameters, and accurate and low-power-consumption baby crying monitoring under strong interference is realized by fusing multi-modal information.