331 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 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.

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 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 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 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 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.

The invention discloses an electric blanket control method and system based on AI voice interaction, and the method comprises the steps: collecting a noisy voice signal through a microphone array, carrying out the filtering of an analog front-end circuit, the echo elimination of an AEC chip, and the noise reduction of a DSP, and obtaining a high-signal-to-noise-ratio pure voice. The AI or voice recognition chip extracts features and matches the features with a pre-stored template, and the master control MCU drives the voltage regulation circuit to regulate the heating power according to an instruction through PWM (Pulse Width Modulation). And dynamically adjusting the power of each region according to the temperature difference by using a closed-loop control algorithm according to ergonomic partitioning. The intelligent electric blanket solves the problems that a traditional electric blanket is inconvenient to operate, inaccurate in temperature adjustment and lack of effective safety protection, and an existing intelligent electric blanket is insufficient in voice interaction function, convenient voice control, accurate partition temperature control and comprehensive safety protection are achieved, and the user experience is improved.

The application discloses a kind of based on DAS and AI's double-branch sound vibration fusion event identification and positioning method, including synchronous acquisition sound and vibration data, construct sound identification branch and vibration positioning branch;Sound branch is extracted multi-scale joint feature by time-frequency domain feature fusion, the weight of dynamic adjustment CNN and BiLSTM is realized adaptive fusion, output event type and confidence;Vibration branch uses four-point space-time weighted optimization method to calculate initial coordinate, position fine-tuning is carried out in combination with ASTCN network, and the final event position is output.The results of fusion double-branch are used, and the probability distribution verification is carried out using dynamic likelihood ratio evaluation mechanism, and the final judgment result is output;The application realizes intelligent event classification by sound identification branch, high-precision event space-time positioning is realized by vibration positioning branch, and intelligent collaboration and system optimization of multi-modal data are realized through fusion and verification module, effectively break through the bottleneck of traditional technology in identification accuracy, positioning error and system robustness.

The invention belongs to the field of voice recognition, and discloses a voice recognition method based on an acoustic model, computer equipment and a storage medium. The method comprises the following steps: acquiring voice features of to-be-recognized voice; inputting the voice features into an acoustic model, and outputting a recognition result by the model; wherein the time sequence processing network layer firstly determines the ratio of current input future frames needing to be pre-watched to context information through a pre-trained gating fusion unit, then calculates the number of the future frames needing to be pre-watched based on the ratio, obtains the corresponding future frames, calculates long-time context representation in combination with the future frames, processes the long-time context representation and outputs the long-time context representation to the next layer of network. According to the method and the device, the problem of static binding of delay and accuracy in the prior art is solved by dynamically adjusting the number of the future frames to be pre-watched, low-delay response to simple command words is realized, the recognition accuracy is improved through multiple future frames to be pre-watched for easily-confused instructions, the balance of the delay and the accuracy is realized, and the performance of a voice recognition system and the user experience are improved.

The utility model relates to the technical field of dam spreading pig breeding, and discloses a pig house comprehensive environment automatic measuring device, which comprises a hanger rail and a breeding inspection machine, and the breeding inspection machine comprises a conveyor running on the hanger rail and an inspection box; an inspection camera, a programmable logic controller, an image analysis module, a sound recognition module, an environment sensing module, a communication module and a power supply module are mounted on the inspection box; the environment sensing module comprises a temperature and humidity sensor, an ammonia gas sensor and a carbon dioxide sensor; the environment parameters, such as temperature, humidity, ammonia gas and carbon dioxide concentration, of the pig house are monitored in real time through the sensors and automatically adjusted according to needs so as to maintain the optimal breeding environment, the breeding environment is optimized, healthy growth of pigs is promoted, diseases are reduced, the breeding quality is improved, an automatic system reduces dependence on manual inspection and management, and the breeding efficiency is improved. And the labor cost is reduced.

The invention discloses a hierarchical tag generation method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring audio information in a vehicle-mounted environment; performing voice recognition on the audio information, and extracting text information; in response to a received first prompt instruction, constructing a label to generate guide information; generating a plurality of basic tags of the text information by utilizing a first preset large model based on the tag generation guide information; in response to a received second prompt instruction, constructing label classification guide information; and based on the label classification guide information, generating hierarchical information of the plurality of basic labels by using a second preset large model. The first prompt instruction guides the generated basic label to capture deeper semantic association of the audio information, the second prompt instruction guides the label classification to find the implicit relationship between the labels, and the isolated labels establish a label system with hierarchical relationship. And the accuracy of the application of the tag in the fields of personalized recommendation and vehicle-mounted safety is improved.

A sound-recognition-based method for monitoring an operating condition abnormity of an oil pump electric motor and outlet pipes thereof. Applying soundprint recognition technology to a speed-regulating hydraulic system of a hydropower station, and rational layout of on-site soundprint sensors realize real-time monitoring of operating conditions of an oil pump electric motor and outlet pipes thereof, thereby improving the intelligence level of device management, and achieving an industrial demonstration effect. By means of deep learning analysis of a soundprint fault sample library combined with multi-dimensional factors such as pressure, liquid level and temperature, a soundprint-recognition-based multi-dimensional-factor logical determination process for abnormal operating conditions of an oil pump electric motor and outlet pipes is designed, such that real-time monitoring and rapid locating of abnormal operating conditions such as base vibration, lubricating oil deficiency in an oil pump electric motor, pump body blade fracture, check valve malfunction, incomplete closure or internal leakage of a drain valve, frequent loading and unloading of an oil pump, and servomotor pipe pulsation are realized, so as to promptly use response measures such as automatic switching of main and standby oil pump electric motor units or automatic adjustment of unit load. Using soundprint sensors and soundprint recognition technology to monitor the state of on-site devices in real time eliminates uncertainties in manual inspection, and improves the fault diagnosis and analysis efficiency.

The invention discloses a remote recognition system based on spatial respiratory tract abnormal sound, and relates to the technical field of sound recognition. The multi-mode sensor array is used for collecting breathing sound in a non-contact mode and obtaining sound source space information of the breathing sound, the cross infection risk is eliminated, and the limitation that a traditional stethoscope can only collect sound signals is broken through. The spatial audio processing module performs preset audio processing on the breathing sound to obtain a breathing sound signal, and obtains sound source control characteristics based on the sound source spatial information; the feature extraction module extracts time-frequency joint features and nonlinear dynamic features from the breath sound signals, and performs feature fusion on the time-frequency joint features and the nonlinear dynamic features and sound source control features to obtain multi-dimensional feature vectors; and the abnormal sound recognition model performs abnormal sound type recognition and spatial positioning on the multi-dimensional feature vector. Therefore, the environmental sound interference can be effectively solved, the type and position of the breathing sound can be intelligently identified, detected and positioned, the identification accuracy of the breathing sound is effectively improved, and meanwhile, the dependence on medical staff is also reduced.

The invention relates to the technical field of audio processing, and discloses a voice recognition method and system based on a neural network, and the method comprises the steps: firstly obtaining a real-time audio stream of a target scene, extracting a time-frequency feature group, analyzing a pulse coding sequence, and constructing a voice network architecture; identifying an acoustic feature cluster by using the acoustic feature cluster, matching a preset framework hierarchical topological relation, and optimizing node weight to generate an adaptive mapping network; based on this, analyzing a sound source propagation path, identifying a multipath effect factor, calculating a frequency domain coupling coefficient, and performing hierarchical fusion to obtain a mixed feature tensor; querying a tensor adaptive response trajectory, extracting a phase distortion feature and a signal-to-noise ratio index, and calculating a signal-to-noise attenuation entropy; and finally, determining a robust recognition level, reconstructing a sound source acquisition dimension and formulating a sound recognition scheme. According to the invention, the voice recognition precision in a complex environment can be improved.

The invention provides a livestock and poultry abnormal sound recognition and positioning method and system, and relates to the technical field of livestock and poultry breeding monitoring, and the method comprises the steps: extracting the sound characteristics of a livestock and poultry sound signal, carrying out the abnormal audio recognition according to the characteristic pattern of the sound characteristics, and obtaining the sound type of the sound signal; when the sound category is abnormal audio, calculating a generalized cross-correlation delay inequality to determine a sound source position range of the sound signal; and calling a particle swarm optimization algorithm to search the generalized cross-correlation delay inequality to obtain a distance value of an optimal sound source position of the sound signal, calling an SRP-PHAT algorithm to search to obtain a sound source position space point of the sound signal in a sound source position range, and finally determining a three-dimensional position coordinate when the livestock and poultry make a sound. Through the method and the device, the defects that livestock abnormal audio recognition is easily interfered by factors such as external environment noise and the like in the prior art, and the sound source positioning precision is limited in a multi-sound-source scene are overcome.

The invention provides a power plant equipment sound abnormity identification and health prediction method based on granular computing and an LSTM network, and the method comprises the steps: employing an array pickup and a high-dynamic-range microphone array in a complex and high-noise background environment of a power plant, and combining an anti-interference filtering and beam forming algorithm, thereby achieving the sound abnormity identification and health prediction of the power plant equipment. According to the method, directional, multi-channel and non-contact sound acquisition is carried out on key parts of equipment, acquired sound signals are preprocessed, converted into time domain, frequency domain and time-frequency domain representations and coded into multi-dimensional numerical vectors, and compared with a rule-based expert system, the method has higher generalization ability and real-time response ability; a large language model is introduced, so that the output is closer to an engineering language and is suitable for operation and maintenance personnel to understand and execute; a self-defined knowledge base or safety semantic filtering is supported, and closed-loop deployment in an industrial field is facilitated; the system can be in butt joint with an intelligent inspection system, and full-link linkage of voice recognition, health assessment and suggestion generation is achieved.

The invention discloses a cough sound recognition method based on a PSO-GBDT-LR model, and belongs to the technical field of signal recognition. The method includes acquiring audio signals; de-noising the audio signal by using a Berouti spectral subtraction method to obtain a de-noised audio signal; the audio event detection VAD is used for segmenting the part, where the sound appears, of the audio; 7-dimensional time domain features are extracted from each segmented audio sample; performing short-time Fourier transform (STFT) on each segmented audio sample, and extracting two-dimensional frequency domain features from a frequency spectrum; combining the extracted 7-dimensional time domain features and the extracted 2-dimensional frequency domain features to form a 9-dimensional feature vector combination; and marking the feature vector of the cough audio sample as a class 1, and marking the feature vector of the non-cough audio sample as a class 0. According to the method, the problems of excessive noise features and abnormal features of the sound in the cough sound recognition process are solved, the features of the cough and non-cough sound can be accurately distinguished, and the generalization ability is high.

The invention relates to the technical field of vehicle safe driving, in particular to an alcohol detection and vehicle control system for safe driving of a driver, which comprises an alcohol detection system, a core control system and an application monitoring system, the alcohol detection system comprises an air pressure detection sensor, a sound recognition sensor, a passive alcohol detection sensor, an active alcohol detection sensor, an infrared body temperature sensor, a camera, an NFC module and a fingerprint recognition module; the core control system comprises an ignition switch control circuit, a 4G communication module, a positioning module, a power supply circuit, a voice module, an automobile CAN bus module and a master control core system. The application monitoring system is connected with the cloud application system through the Internet, and the collected local data and the detection result are reported to the server application monitoring system, so that the risk of alcohol in the body of the driver to safe driving can be reduced.

The invention discloses a construction and recognition method for a sound source recognition network of vehicles in a highway tunnel, and the method comprises the steps: setting a distributed microphone array in the tunnel, and collecting a vehicle audio signal; a MobileNetV3 model based on the Mel frequency spectrum is constructed; the Mel spectrum feature extraction module is used for taking the Mel spectrum feature extracted by the Mel spectrum feature extraction module as input, taking a vehicle audio signal type as output, and training a MobileNetV3 model based on the Mel spectrum to obtain a sound recognition model; constructing a sound source localization model; according to the method, the MobileNetV3 model based on the Mel frequency spectrum is adopted for sound recognition, the sound source positioning model is constructed in a combined mode for sound source positioning, the MobileNetV3 model and the sound source positioning model work cooperatively, the feature description capability of abnormal sound of a vehicle is enhanced, the influence of tunnel echo and environment noise on recognition is effectively reduced, accidents in the tunnel are found in time and positioned accurately, and the method is suitable for popularization and application. The recognition and positioning accuracy of the vehicle sound source in the complex tunnel environment is improved, and the technical problem that in the prior art, the recognition precision of the accident sound in the tunnel is not high is solved.

The invention relates to the technical field of power cable fault detection and positioning, and provides a power cable fault discharge sound recognition method and system based on multi-characteristic fusion, and the method comprises the following steps: obtaining a sound signal of a power cable, and carrying out the adaptive framing windowing processing according to a period peak value; calculating an envelope variance of the sound signal and a local maximum attenuation coefficient; extracting a high-frequency modal energy ratio of the sound signal and calculating a normalized spectrum entropy; inputting the local maximum attenuation coefficient and the envelope variance data into a first classifier; inputting the high-frequency modal energy ratio and the normalized spectral entropy into a trained second classifier; and fusing the two classification results. Based on a self-correlation analysis self-adaptive windowing method and a multi-dimensional feature fusion discharge sound recognition strategy, a dual-classifier structure is constructed, and time-frequency information is fused, so that the accuracy and robustness of cable fault discharge sound recognition are effectively improved.

The invention discloses a sound coding and recognition method and system for biological auditory inspiration. The method comprises the following steps: firstly, constructing a bionic auditory sensor chip constructed based on an organism auditory receptor; then, a bionic auditory coding model is constructed according to an electrophysiological response mode of the organism auditory receptor under the stimulation of sounds with different frequencies and intensities; and finally, coding the sound to be recognized into pulse information by using the coding model, processing the coded pulse signal through a pulse neural network, and finally realizing the recognition of the sound. According to the invention, the auditory feeling process of mammals and the information processing mode of the mammals are simulated, and a pulse coding and sound recognition algorithm with biointerpretability and high efficiency is constructed by fully utilizing the coding of sound information by organisms. Compared with an existing voice recognition method, the voice recognition accuracy is higher, and meanwhile the requirement for computing resources is low.

The invention discloses a wild boar activity intelligent early warning and grading prevention and control method based on AI and thermal imaging, a sensing system carries out thermal imaging processing and camera shooting monitoring on wild boars in corresponding areas in the air through thermal imaging and camera shooting functions of an unmanned aerial vehicle, and a ground infrared camera array carries out infrared imaging processing on the wild boars around the ground; the sound sensor recognizes the sound of the wild boars, and the ground vibration sensor detects the running of the wild boars and the digging of the ground, and relates to the technical field of early warning, prevention and control of the activities of the wild boars. According to the wild boar activity intelligent early warning and grading prevention and control method based on AI and thermal imaging, a sensing module is arranged in a system, and an unmanned aerial vehicle module, a ground infrared imaging module, a sound recognition processing module and a ground vibration sensing module are matched with one another to perform thermal imaging processing on wild boars so as to obtain specific information of a wild boar group; subsequent driving and marking operations are facilitated while continuous monitoring is carried out, and the danger level of the wild boar herd is analyzed through the analysis module.

The invention relates to the field of voice recognition, in particular to an industrial field high-frequency voice recognition method and a storage medium. The method comprises the following steps: performing short-time Fourier transform on an industrial field sound signal by using a double-branch window to obtain a double-branch spectrogram; performing channel stacking on the double-branch spectrogram to obtain a three-dimensional tensor; after the extracted features are classified and calculated, a classification head outputs score vectors of probability scores of three dimensions of target sound, environment sound and strong noise; softening the probability score by using a temperature coefficient, and inputting the softened probability score into a classification function to obtain probability distribution; and calculating an energy score, when the energy score is lower than a preset energy threshold value, calculating an attenuation coefficient to carry out scaling suppression on the probability distribution to obtain final probability distribution, judging whether the highest value in the final probability distribution is lower than a rejection threshold value or not, and obtaining an identification result. According to the method, the fault sound detection rate is greatly improved in industrial actual measurement, and the false alarm rate is remarkably reduced.

An object of a system according to an embodiment is to accurately identify the pitch of a sound and provide visual and auditory feedback.SOLUTION: A system includes a sound recognition and analysis unit, a visual display unit, and an auditory feedback unit. The sound recognition and analysis unit identifies the pitch of the sound using the generated AI. The visual display unit visually displays the pitch of the sound identified by the sound recognition and analysis unit. The auditory feedback unit aurally feeds back the pitch of the sound specified by the sound recognition and analysis unit.SELECTED DRAWING: Figure 1

The invention relates to the technical field of image processing, in particular to a road detection data analysis system which comprises an image definition enhancement module, a spectral analysis module, an enhancement real-time auxiliary module, a sign recognition and evaluation module, a sound recognition monitoring module, a weather condition influence analysis module, a road environment mapping module and a dynamic monitoring and anomaly detection module. According to the method, the image reconstruction quality is improved through the deep residual network resolution algorithm, the road material condition is accurately identified by using the hyperspectral imaging technology and the support vector machine algorithm, the maintenance efficiency is improved through the augmented reality technology, and the road sign identification accuracy is improved through the convolutional neural network model and the deep learning technology. The sound signal processing and the deep neural network improve the traffic abnormal event detection rate, the recurrent neural network model predicts the influence of the weather change on the road safety, the full convolutional network and the U-Net model accurately map the road environment, and the optical flow estimation method and the isolated forest algorithm monitor the road abnormal behavior in real time.

The present invention belongs to the technical field of intelligent driving, and specifically relates to a vehicle head-up display method and system for hearing-impaired drivers. It includes the following steps: S1, obtaining sound signals for complex driving environments; S2, constructing a voice recognition model for hearing-impaired drivers; S3, constructing a vehicle head-up display system for hearing-impaired drivers; S4, the driver assists in driving according to the information displayed by the vehicle head-up display system. The present invention enables hearing-impaired drivers to better perceive the surrounding driving environment, improves driving safety, and solves the problem that hearing-impaired drivers cannot perceive the sound information of the surrounding driving environment.

The invention belongs to the technical field of smart home, and particularly relates to an intelligent sleep regulation and control system based on infrared ray and sound monitoring, which integrates infrared ray monitoring, sound recognition, dryness and humidity measurement, luminosity sensing, sound recording, temperature sensing and artificial intelligence analysis technologies. According to the method, the sleep stage, the sleep quality and the sleep posture of the user are accurately recognized, and the indoor environment parameters are automatically adjusted, so that the purposes of optimizing the sleep experience, improving the getting-up comfort level and achieving health early warning are achieved.