81 results about "Noise correlation" patented technology

The invention discloses a machine room monitoring method and system based on a multi-source data fusion intelligent inspection robot, and belongs to the technical field of machine room automatic monitoring, and the method comprises the steps: applying adversarial transfer learning on a four-dimensional fault semantic feature field, and generating a cross-modal causal atlas representing a fault evolution path through a graph neural network; according to the method, a loss function is combined to align feature distribution of a standard machine room and a current machine room, a gradient inversion layer is utilized to force feature distribution alignment of a source domain and a target domain, meanwhile, an attention mechanism and a causal strength weight are combined to generate a cross-modal causal atlas, and a graph neural network further models physical connection, functional dependence and time sequence association between nodes, so that the cross-modal causal atlas is obtained. A causal relationship is coded into an edge weight, noise correlation is filtered through a causal mask, the stability of the causal atlas is improved, and the cross-modal causal atlas can accurately capture a fault propagation path.

The invention discloses a signal-to-noise ratio improvement method based on a multi-core optical fiber dual-channel distributed acoustic sensing system, and the method comprises the steps: carrying out the light splitting of light emitted by a narrow linewidth laser through a first coupler C1, enabling one path of light to generate a pulse signal through an acoustic optical modulator (AOM), enabling the other path of light to pass through a second coupler C2, and enabling the other path of light to pass through a second coupler C1; the pulse light is divided into two beams of transmission light, the two beams of transmission light are injected into a connected sensing optical fiber through two groups of erbium-doped optical fiber amplifiers (EDFA), two groups of filters, two groups of circulators and a fan-in coupler Fan-in Coupler, and the pulse light is subjected to backward Rayleigh scattering in the sensing optical fiber; based on a dual-channel DAS system, a multi-core optical fiber is used as a sensing optical fiber, three cores and four cores of the multi-core optical fiber are connected in series to form two sensing channels, seven-core space division signals are multiplexed, the equivalent self-noise pressure suppression effect of the seven-core space division signals is analyzed, after noise correlation is removed through a time domain dislocation-frequency domain phase shift method, the signal-to-noise ratio gain can be increased to 8.65 dB, and the signal-to-noise ratio gain can be increased to 8.65 dB. And the detection capability of the system on low-frequency weak signals can be improved.

The invention provides a train operation state estimation method and system based on a high-order extended Kalman filter considering noise correlation, and belongs to the technical field of rail transit train operation management. According to the method, correlation analysis between high-order terms is introduced, the filtering precision is remarkably improved, and particularly, excellent performance is shown under a strong nonlinear system; by establishing a multi-mode train state model, the dynamic characteristics of a train in operation modes such as traction, cruising, sliding and braking are comprehensively covered, and a more accurate input basis is provided for a filtering algorithm; by introducing a sliding window error criterion, the filter can quickly switch operation modes according to real-time errors, so that the filter is ensured to keep high adaptability in a complex and changeable operation environment; the comprehensive performance evaluation framework based on the error covariance matrix not only evaluates the mean square error and the mean absolute error, but also quantifies the contribution of the high-order correlation to the precision of the filter, and provides a more scientific and comprehensive evaluation system.

A gear fault diagnosis method based on the joint weighted envelope anti-noise correlation of sub-signals. The method comprises: first, converting an original vibration signal sequence into envelope signals by means of a squaring-low pass filtering-square root calculation process of a signal sequence element by element; then, reconstructing the envelope signals on the basis of different time intervals, so as to obtain a series of sub-signals, and calculating a fault information representation metric of each sub-signal on the basis of L-moment theory indexes; next, assigning a weight to each sub-signal by combining the fault information representation metric and Sigmoid function transformation; then, on the basis of the envelope signals, and reconstructed sub-signals and corresponding weights thereof, calculating a joint weighted envelope anti-noise correlation function of an envelope signal sequence and the reconstructed sub-signals; and finally, determining a characteristic frequency on the basis of the reciprocal of a time interval value corresponding to a characteristic peak in a drawn graph showing the change of the joint weighted envelope anti-noise correlation function along with the time intervals, and finally recognizing a gear fault. By means of the method, a gear fault can be reliably recognized simply on the basis of time domain analysis when a signal length is limited and there is complex noise interference.

The invention discloses an equivalent self-noise pressure suppression method based on a dual-channel distributed acoustic sensing system, and the method comprises the steps: carrying out the light splitting of light emitted by a narrow-linewidth laser through a first coupler C1, enabling one path of light to generate a pulse signal through an acoustic optical modulator (AOM), enabling the other path of light to pass through a second coupler C2, and enabling the other path of light to pass through a second coupler C1; the pulse light is divided into two beams of transmission light, the two beams of transmission light are injected into a connected sensing optical fiber through two groups of erbium-doped optical fiber amplifiers (EDFA), two groups of filters, two groups of circulators and a fan-in coupler Fan-in Coupler, and the pulse light is subjected to backward Rayleigh scattering in the sensing optical fiber; according to the invention, based on a dual-channel DAS system, two fiber cores of the multi-core optical fiber are used as sensing optical fibers, and the space division signals of the two cores are multiplexed, so that the performance that the equivalent self-noise pressure drop is 2.54 dB in the frequency band of 100-1000Hz is realized under the condition that the noise correlation coefficient of the dual-core signals is 0.11, and the detection capability of the system on low-frequency weak signals can be improved.

The invention discloses an orchard unmanned aerial vehicle multispectral image noise restoration method based on a generative adversarial network, and relates to the technical field of multispectral image noise restoration, and the method comprises the steps: synthesizing a noisy sample containing stripes, photon particles, thermal noise, Rayleigh, shielding, compression artifacts, overexposure and waveband crosstalk on a noise-free reference image through a conditional generative adversarial network; a crosstalk matrix T obtained through optical calibration is introduced; based on the canopy / soil / sky semantic partition, main-cooperation-subdivision level superposition is implemented; generating hierarchical labels by taking energy conservation consistency and thermal noise correlation as thresholds; a branch and region self-adaptive repair module corresponding to classification is arranged in the image-to-image generative adversarial network, and joint loss training containing T regularization is adopted; and outputting the repaired image subjected to physical consistency checking. The method gives consideration to visual quality and spectral fidelity, inhibits cross-band pollution, improves the stability of indexes such as NDVI and NDRE, and is suitable for precision agricultural monitoring.

The invention belongs to the technical field of Internet of Things data processing, and discloses a multi-mode Internet of Things sensor data fusion processing method and system. Comprising the following steps: acquiring multi-modal sensor data in a monitoring environment; performing interference source positioning analysis on the data to generate an interference source distribution map; separating interference waveform characteristics of each modal data based on the distribution map; constructing an interference propagation network by using the features so as to quantify the coupling relationship of interference among different modes; performing noise correlation weakening on original data based on the network to obtain decoupled sensor data; performing dynamic fusion analysis on the decoupling data, and constructing a multi-modal fusion feature vector; and finally, accurately detecting a target event or state based on the feature vector. According to the method, the negative influence of noise correlation on fusion is eliminated, and the accuracy and robustness of target detection and the reliability of the system in a complex environment are improved.

The invention relates to the technical field of camera shooting. The virtual reality panoramic dynamic shooting method and system based on multi-lens cooperation are provided, and the method comprises the steps: carrying out the exposure time sequence synchronous control of a multi-lens array, and generating multi-channel image data of synchronous exposure; performing noise feature separation processing on the synchronously exposed multichannel image data to obtain a high-frequency noise component and a brightness component of each lens; constructing a multi-channel noise correlation model, carrying out combined suppression processing on the noise of the overlapped region, and generating a noise-reduced multi-channel image; performing geometric alignment and dynamic splicing processing on the multi-channel image after noise reduction to generate a panoramic dynamic image; and carrying out real-time rendering processing on the panoramic dynamic image, and outputting a low-noise panoramic picture with an adaptive view angle, so as to solve the problems of image dislocation and dynamic object ghosting caused by noise accumulation and time sequence jitter in an overlapping region under multi-lens collaboration.

The invention discloses an image denoising method based on multi-scale feature enhancement and local pixel disturbance. According to the method, features of different scales are extracted by introducing a multi-scale convolution module and cavity convolution, so that the perception capability of complex noise is enhanced. Meanwhile, flat areas are calculated through the local variance of the pseudo clean image, and pixel disturbance is carried out in the areas so as to weaken noise correlation. According to the method, channel attention and space attention mechanisms are combined, and learning of important features is enhanced. In the aspect of loss function design, pixel loss, multi-scale feature loss and local weighting loss are fused. In practical application, a cosine annealing learning rate scheduling and exponential moving average strategy is adopted, and the convergence speed and stability of the model are remarkably improved. According to the method, image details can be effectively reserved, and the method is suitable for denoising tasks of various complex scenes such as natural images and medical images.

The invention provides a signal processing method and device, electronic equipment, a chip and a storage medium, and relates to the field of communication, and the method comprises the steps: carrying out the noise estimation of a receiving signal received by at least one receiving antenna, so as to obtain an initial noise correlation matrix of the receiving signal; according to the initial noise correlation matrix, determining an interference scale factor used for representing the proportion of white noise and interference in the received signal; according to the interference scale factor, correcting the initial noise correlation matrix to obtain a target noise correlation matrix; and performing noise and interference suppression on the received signal based on the target noise correlation matrix to obtain a demodulation signal. Therefore, the difference between the estimated noise correlation matrix and the real noise correlation matrix can be reduced, the accuracy of noise correlation matrix estimation is improved, and on the basis, noise and interference suppression is performed on the received signal based on the accurate noise correlation matrix, so that the finally obtained demodulation signal is closer to the real sending signal.

A time delay measurement method based on noise correlation, mainly for accurately measuring the time delay in the transmission process of frequency signals. It includes a frequency signal source, a frequency signal transmission link, a local frequency reference, a remote frequency reference, a frequency phase comparator, and an error signal arithmetic unit. In the present invention, the noise of the frequency signal is used as the signal source, and the transmission time delay is determined by analyzing the correlation of the error signals. It has the advantages of simple structure and excellent performance, and is expected to provide a convenient and robust method to achieve local high-precision time synchronization.

The invention belongs to the technical field of mining dust collector noise control, and relates to a mining wet dust collector noise reduction method, which comprises the steps of S1, vibration sensor and electromagnetic damper installation, S2, vibration source monitoring and noise correlation analysis, and S3, adaptive damping adjustment and feedback optimization. A three-step closed loop of hardware installation, intelligent analysis and self-adaptive execution and feedback optimization is applied to the mining wet dust collector, real dynamic noise reduction is achieved, an electromagnetic damper and a gradual change damping material work cooperatively, an active and passive composite noise reduction mode is formed, and the mining wet dust collector can adapt to complex working conditions such as underground air quantity change and dust concentration sudden change in real time.

The invention discloses a noise correlation cross-modal matching loss adjustment method and device based on meta-learning. The method comprises the following steps: constructing a pairing sample set containing a multi-modal data pair; quantifying semantic correlation of each multi-modal data pair in the pairing sample set by using a cross-modal model, and selecting an initial loss function; constructing a loss adjustment network, and balancing the noise sensitive loss function and the noise insensitive loss function to obtain an adaptive loss function; optimizing the loss adjustment network by using a meta-learning algorithm; updating model parameters of the cross-modal model on the target noise data set by using the optimized loss adjustment network; and inputting the query target and each object in the matching database into the updated cross-modal model to obtain the similarity of the query object relative to each object in the matching database. The problem that the robustness and the matching accuracy of the cross-modal model are difficult to consider at the same time in the prior art is solved, and powerful support is provided for landing of the cross-modal model in an actual scene.

The invention relates to a beam gain constraint method for a phased array receiver, and the method comprises the steps: placing the phased array receiver in a far-field region as a receiving array, and obtaining the response of each port of the array to a reference signal and background noise through opening or closing a signal generator; a beam forming weighting coefficient is solved in combination with the steering vector and the noise correlation matrix, a corresponding beam forming network is built, a directional diagram and gain of each beam of the array after weighting are measured, a target gain is selected, a corresponding scaling factor is solved in combination with the relation between the target gain and the actually measured gain of each beam, and the beam forming network is built; and correcting the weighting coefficient of each beam by using the scaling factor, and finally achieving the purpose that the gain of each beam is constrained to the target gain. According to the method, an independent beam gain constraint unit does not need to be established, and correction of the weighting coefficient can be realized only by using an original beam calibration system, so that homogenization of each beam gain in a field range of the phased array receiver is met, and convenience is provided for the phased array receiver to realize high-resolution imaging in radio astronomical observation.

In accordance with a first aspect of the present disclosure, a radar system is provided, comprising: a plurality of receive channels; a first radar detector stage configured to detect, through said receive channels, at least one target using a detector threshold; a noise correlation determination unit configured to determine a noise correlation level indicative of noise correlation between the receive channels if the first radar detector stage has detected the target; a detector threshold adjustment unit configured to adjust the detector threshold in dependence on the noise correlation level determined by the noise correlation determination unit; a second radar detector stage configured to detect the target using the adjusted detector threshold. In accordance with a second aspect of the present disclosure, a corresponding method of operating a radar system is conceived.

An electronic device includes a touch detection system and a noise detection system. The touch detection system includes a first capacitive sensing channel, and a touch detection circuitry to perform a series of touch detections based on a capacitance on the first capacitive sensing channel. The noise detection system includes a second capacitive sensing channel, and a noise detection circuitry to perform at least one noise detection measurement, wherein a respective noise detection measurement comprises determining a respective noise-related capacitance measure associated with a capacitance on the second capacitive channel. The noise detection system to determine a presence of a noise condition based on the at least one noise detection measurement, and in response to determining the noise condition, generate a touch detection inhibit signal to inhibit at least one touch detection in the series of touch detections.

Provided is a de-noising method capable of appropriately de-noising data having a plurality of values, such as an image, even when an AI-dedicated chip or the like is used. The denoising method is a method for removing noise from data having a plurality of values, and comprises: an acquisition step (S01) for acquiring data to be denoised, which has a plurality of values and is to be denoised; a first noise generation step (S02) for performing a first denoising process on the acquired data to be denoised, and generating first noise data relating to noise removed by the first denoising process; a second noise generation step (S03) for generating second noise data by inputting the generated first noise data into a learning model generated by machine learning in advance; and a de-noising step (S04) for generating de-noised data from the de-noised data using the generated second noise data.

The invention discloses a dual-channel signal denoising method using a noise correlation threshold, and belongs to the technical field of automation. The method comprises the following steps: framing a dual-channel signal, extracting a power spectrum and a cross-power spectrum by using fast Fourier transform, and updating frequency domain feature estimation through a dynamic smoothing coefficient; and deducing an NCT dynamic calculation formula in combination with the coherence function model and theoretical noise power, and generating a frequency adaptive NCT curve through processing. And mapping the frequency domain NCT into a time-frequency domain wavelet threshold parameter, and processing a signal by adopting multi-scale wavelet decomposition and a soft threshold function to realize frequency domain noise feature and time-frequency domain denoising processing closed-loop linkage. According to the method, through the steps of framing processing, frequency domain analysis, NCT calculation, wavelet denoising and the like, local mutation and detail information of signals can be reserved while noise is removed, calculation efficiency is improved, and denoising robustness is enhanced.

The invention provides an activated carbon exhaust device and a design method thereof, and belongs to the technical field of exhaust devices.The activated carbon exhaust device comprises an air inlet, a mesh screen, an activated carbon layer, a first breathable filter membrane, a second breathable filter membrane and an air outlet, and the design method comprises the seven steps that firstly, the thickness of the activated carbon layer is determined according to the purification efficiency requirement; calculating the diameter of through holes of the mesh screen through a particle interception equation, and determining the thickness of the screen by adopting a structural strength equation; then optimizing concentric circle distribution of the through holes by using a circular boxing algorithm, and calculating the distance between the through holes through a boxing constraint equation; then determining the air permeability and the membrane thickness of the first breathable filter membrane; then air outlet parameters are determined through an air speed and noise correlation equation, and second filter membrane parameters are calculated through an air outlet resistance matching equation; establishing a double-layer filter membrane optimization equation to determine a membrane thickness combination; and finally, constructing a multi-objective optimization model to optimize each parameter, and determining a final assembly scheme.

A method of improving an SEM image includes (a) measuring a first SEM image, (b) determining a noise correlation length with respect to the first SEM image, (c) based on the noise correlation length being greater than 0, adjusting an aperture signal of an SEM equipment, and repeating (a), (b) and (c) until the noise correlation length is substantially 0.

The application provides a test method, device and equipment for the correlation between vehicle air tightness and in-vehicle noise. For a to-be-tested vehicle placed in a whole vehicle semi-anechoic laboratory, different air tightness states of the to-be-tested vehicle are set. A simulated sound source is played at a preset position outside the to-be-tested vehicle to simulate the noise in the whole vehicle semi-anechoic laboratory during the running of the to-be-tested vehicle, and air tightness data and in-vehicle noise information of the to-be-tested vehicle in different air tightness states are obtained. Based on the obtained air tightness data and in-vehicle noise information of the to-be-tested vehicle in different air tightness states, a mapping relationship between different air tightness data and in-vehicle noise information of the to-be-tested vehicle is determined. In this way, the efficiency of obtaining air tightness data and noise data and the accuracy of determining the correlation between air tightness data and noise data can be improved.

The invention discloses a semi-automatic test method for an energy storage prefabricated cabin, and belongs to the technical field of intelligent testing, and the method comprises the steps of test system construction, semi-automatic testing, multi-physics coupling test analysis and test report generation. According to the scheme, a semi-automatic test process is constructed, automatic acquisition and manual verification are combined, a load sequence and an acquisition instruction are preset in an automatic stage, three-dimensional temperature gradient distribution is generated, a noise spectrum and heat dissipation efficiency are weighted A, and fan rotating speed strategy adjustment, noise source positioning and abnormal data labeling are performed in a manual verification stage. A data foundation is laid for multi-physics field analysis; dynamically adjusting noise confidence and sample weight, generating an adversarial fusion threshold, adaptively adjusting the base classifier weight and the sample weight, establishing a multi-physics field coupling test model, outputting a heat dissipation performance level, a noise compliance judgment result, a coupling risk level and a heat dissipation-noise correlation index; and decision support is provided for optimization and safe operation of the prefabricated cabin.

The invention discloses a compressor low-noise operation rotating speed calibration method and device and a storage medium, and relates to the technical field of compressor control, the compressor low-noise operation rotating speed calibration method comprises the steps that rotating speed acceleration noise associated data is acquired, and a to-be-evaded rotating speed threshold value is determined according to the rotating speed acceleration noise associated data; whether the whole vehicle meets a static condition or a dynamic driving condition or not is determined, the vehicle meeting the static condition is in a static state, and the vehicle meeting the dynamic driving condition is in a driving state; when the whole vehicle meets the static condition, compressor noise data and compressor vibration data are determined; when the whole vehicle meets the dynamic driving condition, environment noise data and environment vibration data are determined; and according to the compressor noise data, the compressor vibration data, the environment noise data, the environment vibration data and the to-be-evaded rotating speed threshold value, data matching is conducted, and the target rotating speed of the compressor is obtained. The effect of accurately calibrating the low-noise operation rotating speed of the compressor is achieved.

The application discloses a kind of adaptive observer-based closed-loop system anti-interference joint estimation method, it belongs to adaptive estimation field.The application solves the problem that it is difficult to obtain the parameters, state and disturbance of controlled object in real time accurately in closed-loop feedback structure.The estimation method of the application does not need prior information of controlled system parameters and disturbance, only utilizes the input and output measurement data and controller information of system, decouples control input and noise, solves the biased estimation problem caused by noise correlation in closed-loop structure;It has robustness to unknown disturbance and noise, and the adaptive observer-based closed-loop system anti-interference joint estimation problem is constructed by using the technology of relatively prime decomposition, and the signal unrelated to measurement noise is used as the reference input described by the stable image of controlled system, then a closed-loop robust adaptive estimation mechanism is built, so as to realize unbiased estimation of unknown parameters, state and disturbance of system.The method of the application can be applied to closed-loop system anti-interference joint estimation.

The application provides a graph matching method for noise correlation, relates to the technical field of image matching, and comprises the following steps: initializing parameters of an online network and a momentum network, obtaining an input image and key points of the online network, inputting the online network to obtain key point representation of the online network, updating parameters of the momentum network, inputting the momentum network to obtain key point representation of the momentum network, calculating back propagation loss based on the key point representation of the online network and the key point representation of the momentum network to optimize the online network, repeatedly iterating until the online network converges to obtain node representation of the online network, obtaining a similarity matrix between nodes through the node representation of the online network, and calculating the similarity matrix through a Hungarian algorithm to obtain a key point matching matrix as a final matching result; the point alignment and edge alignment loss of the graph matching are calculated, the robustness of an image matching model to real noise correlation data is improved, and the accuracy of image matching is significantly improved.

The application discloses a computer room monitoring method and system based on a multi-source data fusion intelligent inspection robot, belongs to the technical field of automatic computer room monitoring, and comprises application of adversarial transfer learning on a four-dimensional fault semantic feature field, generation of a cross-modal causal graph atlas representing a fault evolution path through a graph neural network, and the like, wherein the application aligns the feature distribution of a standard computer room and a current computer room through a joint loss function, forcibly aligns the feature distribution of a source domain and a target domain through a gradient reversal layer, generates a cross-modal causal graph atlas in combination with an attention mechanism and a causal strength weight, the graph neural network further models the physical connection, functional dependence and time sequence correlation between nodes, encodes the causal relationship as an edge weight, filters noise correlation through a causal mask, and improves the stability of the causal graph atlas, and the cross-modal causal graph atlas can accurately capture a fault propagation path.

The present invention relates to an arbitrary-order robust superdirective beam optimization method applicable to circular-ring arrays. Based on the block-circulant property of the noise correlation matrix of circular-ring arrays, eigenvalue decomposition is performed, and the optimal weight vector in the element domain can be transformed into the optimal weight vector in the eigenbeam domain. Then, a coefficient is added in front of the highest-order integer-order weight vector, and by adjusting this coefficient, arbitrary-order superdirectivity can be obtained. The method disclosed by the present invention overcomes the deficiencies of only being able to obtain integer-order superdirectivity or only being able to obtain the fractional-order superdirectivity index by using the desired beam fitting. A more flexible arbitrary-order superdirective beam is obtained. Compared with the methods disclosed in the prior art, it is an arbitrary-order robust superdirective beam optimization method applicable to circular-ring arrays with arbitrary-form subarrays, with less computational complexity and a wider application range.

The invention provides an electric power marketing data cleaning and deduplication method and system, and the method comprises the steps: calculating the neighborhood density and local attribute fluctuation of a data point, and marking the data point as an initial noise point if the neighborhood density is lower than a threshold value and the fluctuation is higher than the threshold value; otherwise, dividing into core points or boundary points according to density; setting an asymmetric expansion neighborhood for the boundary point, calculating the sum of distance attenuation function values from an initial noise point to the boundary point in the neighborhood, and if the sum is higher than a first threshold value, re-classifying as a confirmation noise point; if the initial noise points are located in the overlapped neighborhood and the geometric mean value of the noise correlation indexes of the group of boundary points is lower than a second threshold value, the noise points are classified into secondary boundary points, and the secondary boundary points are classified into a nearest core point cluster; noise points are isolated and confirmed, and the stability is calculated in the cluster; and correcting the distance from the data point to the cluster centroid according to the stability, selecting the minimum corrected distance as a main data record, and combining redundant records.

The invention provides a DSSS parallel code phase capture judgment method based on a noise threshold value, and the method comprises the steps: firstly carrying out the parallel code phase capture of a received signal, and obtaining a received signal code phase correlation value result # imgabs1 # of a # imgabs0 # point; points with the amplitude ranked in the front # imgabs3 # in the correlation value result # imgabs2 # are removed, and a total correlation value set # imgabs4 # about noise is obtained; calculating a noise correlation value mean value # imgabs6 # of a total correlation value set # imgabs5 # of the noise; extracting correlation values higher than the mean value # imgabs8 # in the total correlation value set # imgabs7 #, and counting the number # imgabs9 # of points of the total correlation value set # imgabs7 # to obtain a correlation value set # imgabs10 # of the high-noise region; counting the mean value of the correlation value set # imgabs11 # of the high-noise region to obtain a high-noise average correlation value # imgabs12 #; determining a capture threshold # imgabs13 # by using an average correlation value of high noise; when the highest peak value # imgabs14 # of the parallel code phase correlation value result is stably higher than a capture threshold # imgabs15 #, it is marked that capture succeeds. According to the invention, effective judgment of inconsistent interference signals between correct signals and noise and spreading codes can be completed, so that the capture effectiveness and reliability of a DSSS receiver are improved.

This control device (30) for a glass diaphragm is provided with: an acquisition unit (62) that acquires noise-related information relating to noise flowing into the interior (12) of a vehicle (10); and a control unit (64) that controls the generation of superimposed sound from the glass diaphragm (1), said superimposed sound being superimposed on the noise, on the basis of the noise-related information.