142 results about "Noise threshold" patented technology

The invention relates to the field of concrete crack monitoring, in particular to a solid waste aggregate concrete crack real-time monitoring method and system, and the method comprises the steps: collecting sensor data in real time; calculating a deviation value between the current data and the initial reference data; when the deviation value exceeds a threshold value, the deviation value is used for compensating the subsequently collected sensor data; updating the initial reference data by using the mean value of the compensated data in the previous period; processing the compensated data in different channels, extracting strain accumulation trend characteristics in a long-term channel through a large time window, and extracting acoustic emission instantaneous characteristics in a short-term channel through a small time window; setting a long-term feature weight to be higher than a short-term feature weight, calculating a long-term feature change rate, and when the change rate is stable, improving the long-term weight and reducing the short-term weight, and filtering features lower than a noise threshold in a short-term channel; and fusing the weighted long-term features with the effective short-term features, and inputting the fused features into a pre-training model to obtain a fracture state prediction result in a future predetermined period.

The invention discloses a self-adaptive filtering method and system for an intelligent electric energy meter, and relates to the technical field of power grid signal processing, and the method comprises the steps: obtaining a power grid voltage and current signal set collected by a target intelligent electric energy meter historically, and obtaining a signal noise spectrum data set and a noise level evaluation result through spectrum analysis and noise level evaluation; if the result exceeds a preset noise threshold value, identifying an interference type and building a noise adaptive filtering multi-channel, integrating and deploying the noise adaptive filtering multi-channel into a target intelligent electric meter to complete signal filtering test and effect evaluation, and dynamically optimizing and generating an electric energy meter adaptive filtering multi-channel based on the obtained multi-channel filtering effect index parameter; and finally, signal filtering closed-loop control is realized. The technical problem that a traditional electric energy meter is weak in filtering pertinence and cannot adapt to dynamic noise of a power grid in electric power related scenes with complex dynamic interference is solved, and the technical effects that the electric variable measurement accuracy of the electric energy meter is improved, and the adaptive capacity of the electric energy meter to different interference scenes is enhanced are achieved.

The invention provides a force direction feedback system and method of pneumatic pressure. The system generates a first oscillation signal changing along with air pressure and a second oscillation signal with fixed frequency through a signal conversion module; and the counter counts the cycles of the two paths of oscillation signals. The back noise calibration module samples environment back noise by using a second oscillation signal in a frequency division manner, and eliminates back noise interference in a force count value through a subtractor, so that the problem of detection deviation caused by environment air pressure fluctuation is solved; and the dual-threshold comparison module screens the difference signal through a back noise threshold and an anti-shake threshold, generates a force conversion value, and avoids error output caused by slight fluctuation and shake. The differential operation module processes the force conversion value in combination with the second oscillation signal, the accurate pneumatic force value is output through signal correlation, the pneumatic direction can be distinguished by means of comparison with the fixed reference, and finally accurate and stable feedback of the pneumatic pressure force and direction is achieved.

The invention relates to the field of image processing, in particular to a fish and shrimp denoising enhancement and intelligent identification method based on a two-dimensional sonar image, which comprises the following steps: extracting a sonar image water background, dividing regions and calculating a signal-to-noise ratio, dynamically adjusting a noise threshold, and denoising to obtain a salient image. And a multi-rotation-angle convolution kernel set is constructed based on a size statistics preset ellipse parameter. And clustering salient image pixel points, setting a bounding rectangle as a suspected target area, and judging the consistency of continuous frame targets through an optical flow algorithm. And dynamically adjusting a convolution kernel weight enhancement image, training a YOLO model to recognize a target, superposing recognition results and providing statistical information. According to the method, the fish and shrimp targets with variable sizes and directions in the sonar image are accurately identified and tracked by utilizing dynamic noise threshold adjustment and a multi-angle convolution kernel set in combination with a denoising enhancement technology based on a signal-to-noise ratio and an optical flow algorithm, the limitation of a traditional method is effectively overcome, and reliable technical support is provided for target detection in a complex underwater environment.

The invention discloses a seabed obstacle identification method based on multi-beam point cloud data and related equipment. The method comprises the following steps: acquiring the multi-beam point cloud data of a target area; performing statistics on the basis of the elevation difference between the adjacent sounding point clouds to obtain a frequency-based elevation difference histogram; fitting an attenuation model according to the elevation difference histogram, determining a noise threshold value based on the attenuation model, and dividing the multi-beam point cloud data into a real terrain point cloud and a noise point cloud by using the noise threshold value; a multi-beam real terrain point cloud is obtained through iterative denoising division; and based on the multi-beam real terrain point cloud, obstacle identification is carried out by using the elevation slope, and obstacle information of the target area is obtained. According to the method, through the automatic processes of determining the noise threshold value through the fitting attenuation model and iterative denoising, a traditional interactive filtering method depending on experience of technicians is replaced, the data processing efficiency is greatly improved, and the method can be widely applied to the technical field of data processing.

A voice mixing conversion method includes: performing a noise reduction processing on the initial generated speech based on a noise threshold to generate a post-noise reduction speech; calculating a first quality score and a second quality score, wherein in response to a number of the initial generated speech being 1, the first quality score is calculated based on the initial generated speech, and the second quality score is calculated based on the post-noise reduction speech; and determining whether the first quality score is greater than the second quality score, wherein in response to the first quality score being greater than the second quality score, the initial generated speech is output, otherwise the post-noise reduction speech is output.

A weather prediction method includes generating radiance differences as a difference between measured radiances from satellites and forecast satellite radiances generated by a radiative transfer model and forecasted state profiles output by a numerical weather prediction (NWP) model. When the radiance differences exceed a noise threshold, the method includes generating updated state profiles by generating radiance-sensitivities using a Jacobian model and the forecasted state profiles; constructing a Kalman-gain matrix from background error covariance (BEC) matrices and the radiance-sensitivities; generating filtered state-profile changes from the Kalman-gain matrix and the radiance differences; updating the state profiles by adding the filtered state-profile changes to the forecasted state profiles to yield updated state profiles.

The invention discloses a gas insulation cabinet partial discharge monitoring method and device based on edge calculation, and relates to the technical field of gas insulation cabinets. The method comprises the following steps: collecting environmental data and background electromagnetic noise signals of the gas insulation cabinet, calculating an environmental noise correlation coefficient by using a maximum information coefficient method, combining an environmental deviation value to obtain a comprehensive environmental disturbance index, and correcting a preset background noise threshold value to obtain a corrected noise threshold value; if the real-time electric signal amplitude exceeds the corrected noise threshold, marking the data as partial discharge signal data; denoising the partial discharge signal data by adopting an improved ensemble empirical mode decomposition adaptive noise method, and extracting a discharge feature vector; calculating the mahalanobis distance of each feature vector in the discharge feature vector sequence, obtaining the discharge similarity, obtaining a homologous discharge link, extracting the feature data of the homologous link, inputting the feature data into a BP neural network model based on a dung beetle optimization algorithm, outputting the type of partial discharge, and carrying out the early warning, thereby achieving the monitoring of the partial discharge of the gas insulation cabinet.

The invention discloses a household appliance control method, a household appliance and a storage medium, and relates to the technical field of household appliances, the household appliance control method comprises the following steps: determining user parameters of a user in a working area according to image data and / or audio data corresponding to the current working area of a target household appliance, obtaining a transfer coefficient between the target household appliance and the user; determining a noise threshold value corresponding to the user according to the user parameter; determining a noise value corresponding to the position of the user according to the current working condition of the target household appliance and the transfer coefficient; and if the noise value is greater than the noise threshold value, adjusting operation parameters of the target household electrical appliance so as to reduce noise generated by the target household electrical appliance and improve the use comfort of the household electrical appliance.

The invention discloses a waveform feature extraction method and system for irregular waveforms of a hyperspectral laser radar, and aims at solving the problem that asymmetric waveforms, weak echoes and overlapped echoes in multi-channel full-waveform laser radar signals are difficult to accurately decompose, modeling full-waveform signals collected by multiple spectral channels into superposition of echo components and background noise. A multi-channel waveform is preprocessed through noise threshold and pulse width combined constraint, the waveform position is determined by adopting a gravity center discrimination method, and time synchronization calibration of each channel signal is realized in combination with a multi-echo quality index. The method further utilizes a cubic B-spline function to carry out modeling on transmitted waveforms of all spectrum channels, carries out weighted accumulation on multi-channel echo signals to enhance weak echo signals, and adopts a multi-channel interconnection decomposition optimization strategy to realize accurate extraction of irregular waveform features under the constraint condition of keeping time parameters of sub-echoes of all the channels consistent.

The embodiment of the invention provides a water pump noise control method, a heat management system, electronic equipment and a vehicle. The water pump noise control method comprises the steps of obtaining a target rotating speed of a motor in response to that a detected noise value of a water pump exceeds a preset noise threshold value; acquiring motor parameters of the motor; calculating feedback torque distribution of the motor through the rotating speed prediction model, determining a target control strategy from a plurality of pre-configured control strategies based on the feedback torque distribution, and outputting a predicted rotating speed and parameter information of the motor; and outputting a corresponding voltage control signal to a driving circuit connected with the motor based on the predicted rotating speed. The change trend of the actual rotating speed is estimated by taking the target rotating speed as the expected value through the rotating speed prediction model to obtain the predicted rotating speed, the controller works according to the control strategy corresponding to the controller parameters, and on the basis, the predicted rotating speed outputs the corresponding voltage control signal to the driving circuit, so that the motor rotating speed of the motor can accurately reach the predicted rotating speed, and the driving efficiency is improved. And a better noise suppression effect is achieved.

This invention discloses a vibration signal denoising and feature extraction method based on wavelet adaptive thresholding, belonging to the field of vibration sensor signal processing technology. The method includes the following steps: Step 1, performing multi-level decomposition of the signal using a filter bank constructed based on discrete wavelet transform; Step 2, adaptively calculating the noise threshold; Step 3, implementing wavelet coefficient shrinkage denoising based on the SureShrink algorithm; Step 4, reconstructing the vibration signal based on quasi-discrete wavelet transform; Step 5, automatically extracting signal features based on a multi-layer recurrent neural network. This invention, through signal decomposition based on discrete wavelet transform and an adaptive thresholding denoising algorithm, reduces the error impact caused by the vibration sensor's own noise and environmental disturbances, retains most of the features of the original vibration signal, and establishes a multi-layer recurrent neural network for the denoised vibration signal to achieve automatic extraction of time features, providing an important foundation for the subsequent development of fault diagnosis and health monitoring algorithms.

This application discloses an intelligent detection method and system for corrosion defects in flood discharge tunnels, relating to the field of tunnel corrosion detection technology. The method includes: generating a cavitation number distribution cloud map of the entire flood discharge tunnel area based on historical operating data; marking areas with cavitation values ​​below a preset cavitation number threshold as theoretical high-risk areas; marking areas with cavitation noise intensity exceeding a preset noise threshold as measured noise active areas; spatially superimposing and comparing the theoretical high-risk areas and the measured noise active areas to determine cavitation confirmation areas, potential cavitation risk areas, and cavitation anomaly areas; outputting a surface damage index through a pre-trained cavitation damage assessment model; calculating a dynamic cavitation risk factor; and generating a structured corrosion defect detection report. This method solves the problems of low detection efficiency, limited detection range, and difficulty in accurately assessing corrosion risk in existing flood discharge tunnel corrosion detection methods.

The embodiment of the invention relates to the technical field of pulse diagnosis, and discloses a pulse analysis method based on a pulse diagnosis instrument, which comprises the following steps: processing a pressure change signal within a set time to obtain an original pulse waveform signal associated with a user; performing high-frequency denoising processing on the first pulse frequency signal according to a set noise threshold value; acquiring all wavelet coefficients associated with the high-frequency detail signal, traversing all the wavelet coefficients, determining that the absolute value of the wavelet coefficient at a certain point is greater than the absolute value of the wavelet coefficient adjacent to the wavelet coefficient, and taking the wavelet coefficient as a signal abrupt change point; generating a corresponding pulse image of the user based on the pulse condition data of the user within a set time range; and inputting the obtained pulse condition graph of the user into a pre-constructed standard pulse condition model to determine a pulse condition type result of the user, and outputting the pulse condition type result of the user. According to the scheme provided by the embodiment of the invention, the user can conveniently, quickly and accurately know the pulse condition and the physique state of the user, and the use convenience is improved.

The application belongs to the technical field of signal processing, and particularly relates to a non-coherent scattering ionospheric parameter non-ambiguous estimation method based on Monte Carlo, aiming to solve the problems of inaccurate parameter estimation and poor robustness in ionospheric parameter inversion in the prior art. The method comprises the following steps: receiving measured echo data, estimating the signal noise level and determining the prior information combination mode; obtaining an expected index based on a signal noise threshold mapping library, and judging whether a preset decision threshold is met; if the preset decision threshold is met, starting an optimization selection strategy based on Monte Carlo, and obtaining a candidate solution set; performing cluster analysis on the candidate solution set, selecting a solution with the optimal fitting degree from the solution cluster containing the largest number of solutions, and taking the solution as the final non-ambiguous estimation value. Through systematic Monte Carlo simulation, the application can quantitatively evaluate the inversion reliability under the current data condition, effectively avoid invalid calculation under adverse conditions such as excessive noise or insufficient prior information, and improve the robustness and success rate of estimation.

The invention provides a vehicle control method, a vehicle and a storage medium. The vehicle control method comprises the following steps: acquiring current geographic position information of the vehicle and environmental noise data around the vehicle; judging whether the geographical location information belongs to a preset mute area or not, and judging whether the environmental noise data is lower than a preset noise threshold value or not; and under the condition that the geographical location information belongs to the mute area and the environmental noise data is lower than a preset noise threshold value, when the vehicle executes locking operation, circuit conduction of the whistling device is blocked. According to the method, based on double judgment of the geographic position and the environmental noise, the scene needing to be prevented from generating noise can be accurately recognized, the vehicle locking prompt tone is automatically turned off, the high self-adaptive capacity is achieved, unnecessary noise pollution is effectively avoided, meanwhile, inconvenience caused by excessive intervention to a user can be prevented, and the user experience is improved. And good user experience is ensured.

The embodiment of the invention provides an adaptive filtering method and device, electronic equipment, a readable storage medium and a chip, and the method comprises the steps: obtaining the distance Doppler matrix data of a millimeter wave radar; determining a bottom noise threshold of at least one distance section according to the data of the plurality of continuous frames; determining the point cloud efficiency; determining an adaptive constant false alarm parameter corresponding to the current frame according to the point cloud effective rate corresponding to the historical frame; executing two-dimensional detection according to the adaptive constant false alarm parameter and the background noise threshold corresponding to the current frame, and determining an original point cloud; filtering invalid point clouds in the original point clouds corresponding to the plurality of continuous frames, and determining optimized point clouds; identifying a scene corresponding to the optimized point cloud, and determining false target features in the scene; and filtering false target features in the optimized point cloud to determine a filtered point cloud. Through the scheme of the invention, the accuracy of millimeter wave radar target detection is improved.

The present application aims to provide a kind of image denoising method based on selective smoothing filtering, comprising the following steps: A, input the gray image of the image to be denoised, establish 3*3 rectangular frame, with rectangular frame moving sweeps the entire image to be denoised, calculate the gradient t of each pixel point in the center of rectangular frame and each pixel point on the edge of rectangular frame after each movement;Set noise threshold x, if |t|≤x, then the rectangular frame is input to step B;Otherwise, the rectangular frame is input to step C;B, set edge detection threshold n, calculate threshold K based on each rectangular frame input in step A;If K≥n, then the root mean square value h of gray value of the rectangular frame is calculated E , replace the gray value of the center point E of the rectangular frame;If K

The invention relates to the technical field of intelligent lock safety protection, in particular to a method capable of enhancing the anti-interference capability of an intelligent lock, and the method comprises the steps: collecting electromagnetic induction signals around the intelligent lock in real time, and intercepting a continuous discrete time sequence as a to-be-analyzed sample sequence when the amplitude of the electromagnetic induction signals exceeds a preset ground noise threshold value; calculating a signal transient impact degree index of the to-be-analyzed sample sequence, determining an adaptive penalty factor of variational mode decomposition based on the signal transient impact degree index, and performing variational mode decomposition on the to-be-analyzed sample sequence by using the adaptive penalty factor, obtaining a preset number of intrinsic mode components and center frequencies corresponding to the intrinsic mode components; by means of the technical scheme, high-frequency attack feature amplification and low-frequency interference feature suppression can be achieved, accurate distinguishing of electromagnetic attack signals and electrostatic interference signals from physical underlying features is achieved, and the problem of false alarm or crash of the intelligent lock caused by static electricity is effectively solved.

This invention provides an intelligent reminder method, device, and system for real-time noise detection and peripheral visual interaction, belonging to the field of human-computer interaction and intelligent environment technology. The invention uses a preset dual noise threshold and configures multimodal interactive device working mode switching rules. It collects ambient sound signals using an analog sound sensor and maps them to real-time decibel values ​​via an exponential conversion function. The real-time decibel value is compared with the dual thresholds to trigger a phased visual and tactile multimodal feedback strategy. Simultaneously, a supporting APP enables data interaction between the device and the cloud, completing multi-dimensional visual display, statistical analysis, and report generation of noise data. It also supports remote device control, personalized threshold settings, and non-confrontational social sharing of noise data reports. The invention also designs an integrated intelligent reminder device with a unified housing, integrating sound sensing, processing control, multimodal feedback, magnetic attraction, and Type-C power supply modules, enabling flexible switching between desk lamp, ambient light, decibel monitoring, and composite modes.

The present application relates to the field of image processing, especially to a fish and shrimp denoising enhancement and intelligent identification method based on two-dimensional sonar images, comprising: extracting a sonar image water body background, dividing regions and calculating a signal-to-noise ratio, dynamically adjusting a noise threshold, and obtaining a saliency image through denoising. Based on size statistics, preset ellipse parameters are constructed, and a multi-rotation angle convolution kernel set is constructed. The saliency image pixel points are clustered, and the circumscribed rectangle is set as a suspected target area. The consistency of the target in the continuous frame is judged through an optical flow algorithm. The convolution kernel weight is dynamically adjusted to enhance the image, the YOLO model is trained to identify the target, the identification result is superimposed, and statistical information is provided. Through the use of dynamic noise threshold adjustment and multi-angle convolution kernel set, combined with the denoising enhancement technology based on the signal-to-noise ratio and the optical flow algorithm, the fish and shrimp targets with variable size and direction in the sonar image are accurately identified and tracked, and the limitations of traditional methods are effectively overcome, providing reliable technical support for target detection in complex underwater environments.

This invention relates to a method for improving the accuracy of UWB positioning data, belonging to the field of precise positioning technology in mines. Specifically, the method involves: setting a noise threshold and an empirical medium constant at the positioning receiver, while the positioning system receives the positioning signal; determining whether multipath effects exist in the received signal; if multipath effects exist, calculating the probability PT of no direct path extraction, and using PT to determine whether a direct path is not extracted due to an excessively high noise threshold; the positioning algorithm performs positioning calculations on the successfully extracted direct paths to obtain positioning coordinates (x0, y0, z0); and feeding the positioning coordinates (x0, y0, z0) into a Kalman filter algorithm for filtering to obtain the filtered result (x0, y0, z0). n+1 ,y n+1 ,z n+1 By comparing (x0, y0, z0) and (x n+1 ,y n+1 ,z n+1 The system determines whether the current positioning data is reliable; if the positioning data is reliable, the positioning value is optimized using Kalman filtering, and the current positioning ends. This invention can improve the accuracy and reliability of UWB positioning data, and improve the precision of underground mine positioning.

The application provides a bearing cross-domain fault diagnosis method based on model-agnostic meta-learning, for the bearing vibration signal after preprocessing, the noise threshold of the bearing vibration signal is determined according to the adaptive threshold network trained by the source domain data, and the bearing vibration signal after removing noise is obtained; according to the bearing vibration signal after removing noise and the inner loop network and the outer loop network of the model-agnostic meta-learning bearing cross-domain fault diagnosis model trained by the source domain, the final bearing fault diagnosis result is obtained; the adaptive threshold acquisition network is used to adaptively generate a signal threshold, without expert knowledge, and the improved threshold function can directly filter out interference, combined with the model-agnostic meta-learning network, the accuracy of fault diagnosis is improved.

The present application relates to a kind of range hood noise reduction method, medium, range hood noise reduction mechanism and range hood, by judging the detected smoke exhaust resistance value greater than or equal to the preset smoke exhaust resistance threshold value, and the detected range hood air inlet place noise value greater than the preset noise threshold value, increase auxiliary air inlet passage air inlet area and reduce fan speed, the air inlet place noise value still greater than the preset noise threshold value, continue to increase auxiliary air inlet passage air inlet area and reduce fan speed, otherwise, again detect the smoke exhaust resistance value, in the smoke exhaust resistance value less than or equal to the preset smoke exhaust resistance threshold value and smoke exhaust resistance value current state duration greater than or equal to the preset time when, make range hood air inlet area restore to the initial state when not increase auxiliary air inlet passage air inlet area. Thus, by adjusting auxiliary air inlet passage air inlet area can utilize silencer to reduce range hood noise, and the area of air inlet passage occupied by silencer is compensated to improve oil fume suction effect.

The invention relates to the technical field of automobile control, in particular to an automobile electric drive abnormal sound control method, device and equipment and a storage medium. The method comprises the following steps: monitoring a wheel speed change parameter of a wheel, and calculating a wheel speed fluctuation parameter of the wheel according to the wheel speed change parameter; the abnormal sound condition of electric drive is judged according to the wheel speed fluctuation parameters; and the torque distribution strategy of the motor drive assembly is adjusted according to the abnormal sound condition of the electric drive. According to the method, the electric drive torque output before the dynamic decision of the wheel speed fluctuation parameter is quantified in real time, the low-amplitude impact working condition (such as passing of a daily deceleration strip) is accurately covered, the small torque is instantly output to suppress gear knocking when the wheel speed fluctuation reaches the abnormal sound threshold value, and the defect that low-amplitude impact cannot be processed due to energy consumption constraint in a traditional scheme is overcome; and meanwhile, torque output is prohibited when fluctuation is not larger than a threshold value, energy consumption economy is effectively maintained, and abnormal sound radical treatment and optimal energy consumption balance under all working conditions are achieved.

The application provides a simplification method, system, device and medium for complex linear map elements, comprising: marking the kinks on the complex linear vector map by estimating the maximum noise threshold size according to the zoom scale; removing the line segments and corresponding kinks between the kinks on the complex linear vector map, whose length is less than the maximum noise threshold size, to obtain a preliminary screened map; starting from the kink at the starting end of the preliminary screened map, constructing a sliding circle with each kink as the center in turn, and screening the kinks according to the number of intersection points between the sliding circle and the line segments, to obtain a screened map; and smoothing the screened map based on the Bezier curve to obtain a simplified map. The application solves the problem that the simplification process in the prior art causes the loss of complex bending features of part of the map elements, and the simplification is incomplete due to the omission of part of the sharp bending and dense kinks during identification.

An example medical device includes a memory; and processing circuitry coupled to the memory, the processing circuitry is configured to: receive, from one or more electrodes coupled to the medical device, a cardiac signal; determine a risk of noise being greater than or equal to a noise risk threshold or an active amount of the noise being greater than or equal to an active noise threshold; and in response to determining the risk of noise is greater than or equal to the noise risk threshold or the active amount of noise is greater than or equal to the active noise threshold, activate a filter to filter the noise from the cardiac signal.

The application provides a hybrid vehicle engine output control method, a vehicle controller and a hybrid vehicle, and the method comprises the following steps: determining whether the noise data in the vehicle is greater than a noise limit threshold; if yes, reducing the output of the engine of the hybrid vehicle to a predetermined range and then executing the subsequent steps; if no, directly executing the subsequent steps. According to the scene parameter data, the current driving scene of the hybrid vehicle is determined, the noise threshold corresponding to the current driving scene is found from a preset scene-noise database, the noise data in the vehicle is compared with the noise threshold, and according to the comparison result, the current driving scene and the battery power, it is determined whether the preset noise control condition is met; if yes, the hybrid vehicle is switched to a pure electric driving state; if not, it is determined whether the preset noise control condition is met. According to the scene and the noise experience of the user, the state of the engine can be controlled, and the electric noise experience of the user is improved.

Systems and methods for noise reduction using synthetic audio are disclosed. One or more playback devices include first microphone(s) (e.g., air-conduction microphone(s)) and second microphone(s) (e.g., bone-conduction microphone(s)). In operation, based on a user voice input, first and second sound data streams are captured via the first and second microphone(s), respectively. The first sound data stream is evaluated to determine whether a noise threshold is exceeded. While the noise threshold is not exceeded, a synthetic sound data model is trained based on the first and second sound data streams. An audio output stream based on the first sound data stream is communicated to at least one second playback device. When noise is detected in the first sound data stream, a synthetic audio stream is mixed into the output audio stream. The synthetic audio stream can be produced based on the synthetic sound data model.