456 results about "Noise removal" patented technology

The invention relates to the field of river pollution treatment, and discloses a water quality treatment method, system, equipment and medium based on a remote sensing inversion technology, and the method comprises the following steps: S1, collecting a hyperspectral remote sensing image of a to-be-monitored water body, the hyperspectral remote sensing image comprising chlorophyll concentration, suspended matter concentration and dissolved oxygen; s2, based on the hyperspectral remote sensing image, preprocessing the hyperspectral remote sensing image, including geometric correction, atmospheric correction, noise removal and waveband fusion, to obtain a preprocessed image meeting an inversion precision requirement; and S3, inputting the preprocessed image into a water quality inversion model, and obtaining pixel-level chlorophyll, suspended solids and dissolved oxygen concentration data based on physical radiation transmission. By combining the hyperspectral remote sensing image and the water quality inversion model, large-range, real-time and high-precision water quality monitoring and pollution zoning are realized, model parameters are dynamically updated, and the timeliness and precision of water quality treatment are improved.

The invention relates to the field of intelligent automatic driving, in particular to a multi-vehicle cooperative controllable confrontation test method based on a diffusion model. Comprising the following steps: S1, a diffusion model training stage: training a diffusion model based on real driving data, and learning vehicle behavior distribution through forward noise addition and reverse denoising to obtain fixed model parameters; s2, scene and multi-vehicle space-time modeling: fusing the road map, the lane topology and the vehicle state information, and constructing a space-time scene representation of multi-vehicle interaction as input of diffusion generation; s3, a diffusion generation mechanism based on adversarial guidance; s4, performing partial diffusion control and diversified confrontation generation; and S5, evaluating the authenticity and controllability of the multi-agent confrontation scene. Compared with the prior art, the method has the advantage that the authenticity, controllability and closed-loop consistency of multi-vehicle cooperative confrontation scene generation are obviously improved.

The invention discloses a low-illumination image enhancement method suitable for a complex night operation scene. The method comprises the following steps: preprocessing a collected low-illumination RGB image to obtain a preprocessed image; multi-scale features are extracted from the shallow convolution projection and the multilayer axial converter unit, and cross-layer attention is used for weighted aggregation to form enhanced features; forward noise addition is executed according to noise scheduling to obtain potential representation representing low-illumination noise distribution, and deep semantic representation is extracted in combination with a U-Net encoder embedded in CBAM; in the decoding stage, details are reconstructed through jump connection and a multi-layer converter, meanwhile, reverse denoising of a diffusion model is introduced to gradually remove noise, and an enhanced image with high brightness and low noise is output; in the training process, bidirectional mapping from low illumination to normal illumination is constructed, a weighted target of self-encoding loss and enhanced loss is introduced to carry out joint optimization on a converter and diffusion parameters, and a low-illumination scene is monitored on a general GPU / edge device.

The invention relates to the cross technical field of signal processing and artificial intelligence, in particular to a multi-source noise removal method and system based on DAE, and the method comprises the steps: 1, carrying out the data collection and feature extraction of multi-source noise and pure signals; step 2, constructing a de-noising recognition knowledge base based on feature analysis; step 3, constructing a deep denoising auto-encoder model based on a knowledge base; step 4, hierarchical training and optimization guided by a mixed loss function of the deep denoising model; step 5, denoising processing of a target signal and output evaluation based on a discrimination model; according to the invention, noise data in multiple fields such as electromagnetism, remote sensing and biological signals are integrated, a dynamic mixing strategy and a data enhancement technology are adopted, a training set which highly simulates a real environment is constructed, and a unique cross-scene adaptation module can perform adaptive adjustment according to signal characteristics of different application scenes; the problem that a traditional method is poor in scene adaptability is solved.

The invention discloses an electrocardiosignal preprocessing system and method based on filtering and deep learning, and relates to the field of electrocardiosignal data processing. The multi-stage adaptive filtering module comprises a baseline drift elimination unit, a power frequency interference suppression unit and a myoelectricity noise removal unit, and all the units are connected in sequence to form pipelined parallel processing; the deep learning fusion module is used for performing deep feature extraction and noise classification on the output signal and feeding back a classification result to the multi-stage adaptive filtering module; the signal quality evaluation module carries out quality evaluation on the electrocardiosignals subjected to multi-stage adaptive filtering and deep learning fusion and judges whether the signal quality is qualified or not; a feature enhancement and standardization module; the technical effects of improving the self-adaptability and robustness, improving the calculation efficiency of heart disease classification, the signal fidelity and the diagnosis reliability, and enhancing the signal quality evaluability and the self-adaptive ability are achieved.

The invention provides a point cloud data processing method and device of a laser radar, equipment and a medium, and the method comprises the steps: carrying out the adaptive local neighborhood construction of each query point cloud, and determining a local neighborhood point set of the query point cloud; determining a plurality of geometric features of each query point cloud based on the local neighborhood point set; performing normalization processing and dynamic weight optimization on the plurality of geometric features of each query point cloud, constructing a comprehensive feature of each query point cloud, dividing the plurality of original point cloud data based on the plurality of comprehensive features, and determining a plurality of point cloud categories; and carrying out noise removal processing on the original point cloud data in each point cloud category, determining updated point cloud data, repeatedly carrying out local neighborhood point set determination and point cloud division processing on the updated point cloud data until a convergence condition is met, and outputting the denoised point cloud data. Effectiveness and anti-interference capability of feature fusion are improved, and intelligent identification and denoising of the point cloud and accurate retention of the effective point cloud are realized.

The invention relates to a human body sensing detection method and system based on machine learning, and the method comprises the steps: extracting target micro-motion change, space trajectory and time sequence features through the collection and high-precision marking of original time-space behavior data of millimeter wave, infrared, pressure and sound multi-type sensors in combination with preprocessing, noise removal and normalization standards, and carrying out the detection of a human body. A human body space-time behavior fingerprint database is constructed in an off-line or on-line state through a machine learning algorithm, and regional difference behavior characteristic models are respectively established for different space regions and installation materials. And automatically triggering multiple rounds of redundancy detection and learning optimization. The scheme reduces the false alarm rate of the system and supports continuous evolution of the model.

The invention discloses a geometric constraint fitting point cloud filtering method for sea surface three-dimensional reconstruction, and belongs to the technical field of computer vision and three-dimensional reconstruction. The objective of the invention is to solve the problem of insufficient subsequent three-dimensional reconstruction precision caused by interference of reflection noise, mismatching points and the like in sea surface point cloud. The method specifically comprises the following seven steps: firstly, acquiring sea surface original point cloud through three-dimensional data acquisition equipment; a filtering technology is adopted to obtain a to-be-fitted point cloud; fitting a quadric surface through an improved RANSAC (Random Sample Consensus) algorithm to solve an initial parameter; constructing a comprehensive error function, and optimizing the model through gradient descent; effective inner points are screened through quadratic term coefficient constraint and a distance threshold value; and finally, iterating until a termination condition is met, and outputting an optimal effective point cloud. The method is high in noise rejection rate, the point cloud fits the sea surface form, and high-quality data support can be provided for sea surface fitting, sea wave simulation and unmanned ship control.

The invention mainly discloses a multi-source exploration geological point cloud three-dimensional solid body model construction method, electronic equipment and a storage medium in the field of geological exploration, and the method comprises the steps: obtaining multi-source point cloud data of a laser radar, an earthquake and the like, and executing coordinate unification, noise removal and attribute normalization based on data quality; projecting to a unified attribute space to realize geometric and physical attribute fusion; building a three-dimensional grid based on the fused data, introducing geological constraints such as faults, performing boundary-preserving interpolation on a boundary region, and performing multi-scale block inversion on an abnormal body region; constructing a voxel model storing spatio-temporal data and compressing the voxel model; and outputting the model through distributed parallel modeling. The method solves the problem of multi-source data isomerism, improves abnormal body modeling precision, supports geologic body dynamic evolution depiction, improves mass data processing efficiency, and is suitable for various geological exploration scenes.

The invention relates to the technical field of face recognition, and discloses a face recognition system and a detection method thereof, and the recognition system is composed of a face image acquisition module, an environment perception and preprocessing module, a face detection feature extraction module, a comprehensive analysis and comparison module and a data storage monitoring module. According to the method, environmental factors such as illumination intensity, light source type and color temperature are detected through a sensor, corresponding preprocessing is carried out on the image, the influence of the environmental factors on the recognition precision can be reduced by correcting illumination unevenness, denoising and enhancing contrast and sharpness, meanwhile, image compensation and denoising are carried out, illumination unevenness is compensated, and denoising processing is carried out, so that the recognition precision is improved. The beneficial effects of comprehensive analysis of age, health conditions and illumination condition environmental factors through face recognition and more stable recognition precision are achieved.

The invention discloses a truck trip chain identification method based on trajectory data, which comprises the following steps: S1, acquiring GPS trajectory data of a truck, and preprocessing the GPS trajectory data, including data cleaning, noise removal and trajectory segmentation; s2, by setting a speed threshold value and a time threshold value, the stop point of the truck is recognized; s3, spatial matching is carried out on the stay point and road network data, and the accurate position of the stay point is determined; s4, segmenting the track of the truck based on the time sequence and the space distribution of the stop points to form a plurality of travel segments; s5, analyzing the driving characteristics and the stop characteristics of each travel section, and removing stop points related to non-freight; and S6, identifying key staying points related to loading and unloading activities, and connecting the key staying points according to a time sequence to form a truck travel chain. According to the method, the actual running path and the logistics movable chain of the truck can be clearly restored, and high-precision technical support is provided for freight path analysis and industrial chain space structure recognition.

The invention relates to a tunneling action generation method and system based on time-space depth fusion multi-task prediction in the technical field of shield engineering data processing, and the method and system integrate local and global time sequence information through a dynamic depth fusion network, achieve the fusion of multiple time-space scales, improve the perception capability of complex working conditions, and improve the efficiency of shield engineering data processing. Meanwhile, future state prediction and control candidates are output, action fusion is carried out on a strategy layer, performance loss caused by prediction-control splitting is reduced, prediction-control integration is achieved, an uncertainty head is introduced, explicit constraint is carried out on a loss function and strategy fusion layer, the risk under stratum sudden change or sensing noise is effectively restrained, and the prediction-control performance is improved. Uncertainty constraint security is realized; on the basis of experience playback, weight self-adaption and noise removal, online self-adaption updating requirements of different stratums and tunneling stages are met, online self-adaption updating can be achieved, and the action generation capacity of tunneling stability control under the complex stratums and noise conditions is remarkably improved.

The invention relates to the technical field of aeronautical text data processing, and discloses an aeronautical text content cleaning and labeling method, system, device and medium wherein the method comprises: noise filtering: identifying and removing noise in an aeronautical text in combination with static cleaning and a general large model; format standardization: converting the aviation text after noise removal into a standardized format text; duplicate removal and error correction: detecting duplicate contents based on a hash algorithm, and correcting spelling errors and grammar errors based on a general large model to obtain an aviation text subjected to duplicate removal and error correction; entity identification: key entities are extracted based on the general large model, and the extracted key entities are labeled; active learning: screening high-value samples in the marked key entities based on an uncertainty query strategy; and dynamic optimization: performing verification and iterative optimization on the marking result in combination with the aviation knowledge base. According to the method, the automation level, the labeling accuracy and the system self-adaptive capability of aviation text processing can be remarkably improved.

The invention discloses a method for identifying interference signals of weld defects under different lifts-off conditions, which comprises the following steps of: S1, de-trending processing: carrying out de-trending processing on original detection signals; s2, Gaussian wavelet transform: carrying out Gaussian wavelet transform on the detrended signal; s3, optimal wavelet basis selection: by calculating correlation coefficients or energy ratios of different wavelet basis and defect signals, selecting the wavelet basis with the highest matching degree for reconstruction; s4, envelope processing: extracting a signal envelope based on Hilbert transform; s5, mean filtering: applying sliding window mean filtering to the envelope signal; and S6, threshold processing: setting a self-adaptive threshold screening signal, and retaining the feature points of which the amplitudes exceed the threshold. According to the method, de-trending and Gaussian wavelet transform are used for de-noising enhancement. According to the method, wavelet functions of different orders are constructed and matched with defects, secondary signal enhancement is carried out by selecting a filtering method, finally, threshold stripping interference is calculated, reliable defect identification is carried out, and technical support is provided for uneven welding seam quality monitoring.

The invention provides a hybrid filtering denoising method and device based on adaptive parameters, and relates to the technical field of three-dimensional point cloud processing. The method comprises the following steps: acquiring three-dimensional point cloud data of a target object; performing voxel segmentation processing on the three-dimensional point cloud data to obtain a plurality of voxels; according to the segmented three-dimensional point cloud data, combining a plurality of voxels by using a density similarity model to obtain a voxel set with different density distributions; based on the merged voxels, a radius filtering parameter and a statistical filtering parameter of each voxel set are adaptively determined, the radius filtering parameter is used for filtering isolated noise in the three-dimensional point cloud data, and the statistical filtering parameter is used for filtering random noise in the three-dimensional point cloud data; performing random noise removal processing on the three-dimensional point cloud data according to the statistical filtering parameters; and performing isolated noise removal processing on the three-dimensional point cloud data after random noise removal according to the radius filtering parameter.

The invention relates to the technical field of photoacoustic measurement, and particularly provides measuring equipment and a frequency estimation method and a denoising method of equipment noise therein, the frequency estimation method comprises the following steps: controlling pump light and probe light to irradiate a first sample to be measured, the pump light generating ultrasonic waves on the surface of the first sample to be measured, the detection light is reflected by the surface of the first to-be-detected sample to form first detection signal light; acquiring a first photoacoustic time domain signal formed by the first detection signal light; removing a non-noise signal and a random noise signal in the first photoacoustic time domain signal to obtain a first time domain noise signal; performing time-frequency transformation on the first time-domain noise signal to obtain a first noise spectrum; and determining the frequency of the equipment noise according to the spectrum amplitude of the first noise spectrum. Thus, the device noise in the photoacoustic signal can be removed in a targeted manner according to the frequency of the device noise subsequently, and compared with a traditional overall noise removal manner, the signal-to-noise ratio and the measurement sensitivity can be improved.

The invention discloses a quick classification method and system for human scars through visual calculation, and relates to the technical field of artificial intelligence, and the method comprises the following steps: S1, scar image collection and preprocessing: obtaining a human scar region image and shooting parameters, and carrying out the noise removal, illumination correction and scar region segmentation of an original image; s2, multi-modal scar feature extraction: based on the segmented scar region, extracting color features, texture features, morphological features and depth features; s3, feature fusion and dimension reduction; s4, constructing and training a lightweight scar classification model; s5, outputting a classification result and dynamically optimizing the model; according to the rapid classification method and system for the human body scars through visual calculation, through multi-modal feature fusion and improvement of a lightweight model, the classification accuracy is better than that of a traditional manual classification and single feature machine learning method, subjective differences of doctors are eliminated, and the classification objectivity is guaranteed.

The invention discloses a special equipment safety monitoring method based on the Internet of Things, and provides a multi-mode sensing data acquisition, noise removal, data standardization and label completion method. Rare abnormal samples are screened through unsupervised clustering and anomaly detection, and diverse pseudo-abnormal data are expanded through a conditional generative adversarial network. And deep representation learning, multi-modal feature mapping and migration fusion are further applied to construct a multi-modal collaborative abnormal feature set, and the multi-modal collaborative abnormal feature set is incorporated into an actively optimized security event discrimination model to realize dynamic rule adaptive evolution.

The invention discloses a weather radar meteorological echo and non-meteorological echo identification system, and belongs to the technical field of meteorological radar signal processing. The method comprises the following steps: acquiring radar original data containing reflectivity factors, radial velocity and spectral width, and generating a three-dimensional feature matrix through format standardization, combined noise removal, neighborhood interpolation filling and parameter normalization; constructing a sample set through professional labeling, consistency verification and multi-dimensional data enhancement; a deep convolutional neural network containing a ResNet50 feature extraction module, a CBAM double attention module and a classification output module is constructed, and a binary cross entropy loss function, an Adam optimizer and an early stop regularization strategy are adopted for training optimization; to-be-recognized data is preprocessed and then input into the model, and a classification result and confidence are output. The method does not need manual feature design, significantly improves the recognition stability of echoes difficult to distinguish in a complex scene, adapts to different radars and regional environments, and meets the real-time processing requirements.

The invention belongs to the technical field of industrial equipment operation and maintenance, and discloses a time series data anomaly detection method and system based on industrial edge equipment, and the method comprises the steps: firstly generating adaptive directional noise, and diffusing a normal window signal and a to-be-detected window signal; secondly, reconstructing a non-abnormal normal window signal through a teacher model; normal window signals after abnormal stripping are reconstructed through a student model; constructing a multi-view fusion anomaly score according to the reconstructed normal window signal without anomaly and the normal window signal after abnormal stripping; and then, according to the multi-view fusion anomaly score, time series data anomaly is detected. According to the method, direction self-adaption of noise can be achieved, noise-denoising-scoring closed-loop consistency is constructed, abnormal amplification is enhanced, high-quality detection is provided for the industrial edge equipment, and safety maintenance of the industrial edge equipment is facilitated.

The invention discloses a swimming skeleton point coordinate data denoising method and device based on spatio-temporal topological structure learning, and belongs to the field of data processing. The method comprises the following steps: acquiring and splicing a swimming video of a target object; detecting a target object in the video, and obtaining skeleton point coordinates and corresponding confidence scores of the target object by adopting a posture estimation model; preprocessing the coordinates to obtain feature vectors; adjacency information of each skeleton point is obtained based on the human body skeleton topology, the feature vectors of all the skeleton points are input into a spatial domain noise removal network, the adjacency information of the skeleton points is aggregated, multi-scale pooling is carried out, and denoised spatial domain features are obtained; sampling the high-dimensional spatial-temporal characteristics by adopting a deformable time convolutional network; and carrying out deformable convolution operation and decoding operation on the sampling features to obtain skeleton point coordinates after time-space domain denoising. Linear and nonlinear noise in data is processed in a time-space domain in a cooperative manner, so that the limitation of a traditional low-pass filter method and an existing ST-GCN method is effectively overcome.

The invention belongs to the technical field of multi-modal image matching, and particularly relates to a multi-modal image matching method based on fusion features and attention state space. The method comprises the following steps: preprocessing an obtained infrared image and a visible light image, wherein the preprocessing comprises geometric correction, noise removal and brightness normalization; performing multi-scale feature extraction on the preprocessed images to obtain multi-scale features; performing rotation scale correction on the features by using a group convolution module based on a rotation group; inputting the corrected features into an attention state space model for cross-modal feature interaction, wherein the model adopts a convolution state updating mechanism and space attention fusion; on the basis of the interacted features, a rough matching result is obtained through block similarity calculation; carrying out refined optimization on a rough matching result, carrying out local feature extraction by using a small convolution kernel, and combining an attention weighting mechanism; in the training stage, a self-adaptive uncertainty loss function supervised model based on Gaussian negative logarithm likelihood is adopted for training.

The invention discloses a self-adaptive intervention type single-array-element photoacoustic endoscopic imaging near-field noise removal method based on a spatial domain and morphological characteristics. The method comprises the following steps that 1, original data of photoacoustic endoscopic imaging are obtained; step 2, adaptively generating a patch mask area; 3, preprocessing the image in a frequency domain; step 4, executing strip interference detection based on adaptive spatial domain filtering on the non-plaque region to obtain a spatial domain noise mask; step 5, screening strip-shaped noise areas; step 6, performing interpolation restoration on the noise area; 7, calculating the convergence degree and the noise level in the iteration process; and step 8, outputting a final result after polar coordinate transformation. The intrusive single-array-element photoacoustic endoscopic imaging method can effectively inhibit strip-shaped interference signals occurring in intrusive single-array-element photoacoustic endoscopic imaging and reduce the covering influence of background noise on target signals, so that the imaging quality of a near-end area is improved, and the structural definition and contrast ratio of overall imaging are remarkably optimized.

The invention relates to the technical field of electric data processing, in particular to an air compressor operation data collecting method and system based on the Internet of Things, and the method comprises the steps that operation data of an air compressor are collected and preprocessed; any moment in any dimension is selected as a target moment, and a sequence of operation data of the target moment in the neighbor time window is acquired; and performing ascending sorting on the instantaneous energy of each moment in the sequence to obtain an instantaneous energy quantum sequence. According to the method, the standard deviation parameter in the Gaussian filtering algorithm is adaptively adjusted based on the global fault confidence, so that the method has relatively high smoothing capability on random noise data, the denoising capability is enhanced, more detailed information is reserved for fault feature data, excessive smoothing is avoided, and the fault detection accuracy is improved. Finally, data which can eliminate noise interference to the maximum extent and can retain real fault features are obtained, and high-quality data are provided for follow-up high-precision state monitoring and maintenance.

The invention relates to the technical field of liquid level reading identification, and discloses a liquid level meter reading identification method based on computer vision. The method comprises the following steps: acquiring and preprocessing an image, acquiring a digital image through image acquisition equipment, and executing normalization, noise removal and contrast enhancement; feature extraction and weight generation: extracting features such as a scale line position and a pointer angle from the preprocessed image, and generating a reading preference weight vector; performing parameter mapping and optimization, mapping the weight vector into an identification control parameter, referring to a predefined parameter anchor point in an offline optimization database, and performing interpolation calculation to adapt to a current image condition; and reading calculation and output: executing a liquid level value calculation algorithm by using the optimized parameters and outputting a result. The method adapts to different image environments through multi-stage cooperation, the adaptability and stability of liquid level reading recognition are improved, and the method is suitable for automatic reading scenes of various liquid level instruments.

The invention provides a low-resource multi-language large model training method and system for personalized course learning, and belongs to the technical field of large language models, and the method comprises the steps: S1, collecting training samples of multiple languages; performing de-duplication and de-noising processing on each sample, then unifying data formats, and adding language attributes as language labels; s2, initializing parameters of an adaptive sampling scheduler and a dynamic loss scheduler; and S3, taking the pre-trained large language model as a base model, and adding an adaptive sampling scheduler and a dynamic loss scheduler in the training process. According to the method, the dependence on low-resource language annotation data is reduced, and the training weights of different language samples can be adaptively balanced; and dynamically matching the multi-language task difficulty with the model learning progress.

A method and system for efficiently suppressing strong interference in artificial current source electromagnetic data with high accuracy is provided. [Solution] The method inputs the artificial current source electromagnetic data to be processed into an artificial current source electromagnetic data classification model to obtain first noise-free artificial current source electromagnetic data and noise-containing artificial current source electromagnetic data, converts the noisy artificial current source electromagnetic data into two-dimensional image data using a dimension conversion function and inputs it into a DWTSC-UNet noise removal network model to obtain the noise-removed data result, performs inverse dimension conversion on the noise-removed data result using the dimension conversion function to obtain second noise-free artificial current source electromagnetic data, and combines the first noise-free artificial current source electromagnetic data and the second noise-free artificial current source electromagnetic data to obtain complete noise-free artificial current source electromagnetic data.

The invention relates to the technical field of image recognition, and particularly discloses a transformer fire stage recognition method based on image recognition. Comprising the following steps of 1, collecting real-time image data of a transformer through a monitoring camera, and monitoring the operation state of the transformer; 2, preprocessing the image data, including noise removal, brightness normalization and target area extraction, so as to improve the accuracy of subsequent recognition; step 3, extracting depth features of the image based on a convolutional neural network, extracting feature parameters such as color distribution, smoke form, flame texture and the like, and performing feature fusion to form a multi-modal feature vector; according to the technical scheme, fine recognition of the fire stages is achieved, the transformer fire development process is divided into six stages by introducing a deep learning model and an image recognition algorithm, fine-grained recognition of the whole fire process is achieved, and the bottleneck that the fire stages are difficult to distinguish through a traditional monitoring means is broken through.

The invention relates to a pediatric depression risk level determination method and device based on a big language model, and the method comprises the steps: carrying out the unified coding, noise elimination, sentence segmentation, part-of-speech tagging, voice-to-text conversion and desensitization processing based on original text data, and obtaining a standardized text; inputting the standardized text data into a preset large language model, and generating a semantic vector through word segmentation embedding; based on the semantic vector, utilizing a preset emotion recognition model to extract a depressive symptom entity and feature items thereof; based on the symptom entity and the feature item, mapping according to a preset depression standard dictionary and a synonym expansion word list to obtain a quantifiable structured feature vector; and based on the structured feature vector, obtaining a risk assessment level of the depression of the children. According to the method, the recognition accuracy of the depressive symptoms in the unstructured text is improved, and the time cost of manual interpretation is reduced.

The pickup device (1) includes: an adaptive filter (141) that generates a speculative noise signal from a reference signal representing the noise signal component contained in the input signal acquired by the microphone (11); a noise removal signal generation unit (15) that generates a noise removal signal after subtracting the speculative noise signal from the input signal; a filter coefficient update unit (142) that updates the filter coefficients of the adaptive filter (141) using the noise removal signal; and a sample position determination unit (162) that determines at least one signal sample position from the signal sample position with the largest absolute value of the noise removal signal up to the predetermined largest signal sample position, wherein the filter coefficient update unit (142) updates the filter coefficients at the at least one signal sample position determined by the sample position determination unit (162).