Removal Of High Frequency Noise Research Articles

High-frequency Noise Removal of Audio Files using Daubechies Wavelet Transform

In general, audio signals are contaminated with various types of noise. This paper presents a novel signal processing method developed for high-frequency noise elimination using wavelet transforms. As a continuation of a previous study that used Fourier transform for noise removal in audio files, in this study Daubechies wavelets were used to reduce computational complexity and achieve better noise reduction performances. Compared to the Fourier transform, the Daubechies wavelet transform method removes the noise in each signal while preserving its vital characteristics. The suitable level of the Daubechies wavelet for noise removal in each channel was obtained using a trial-and-error approach. It was identified that the ideal range for the level of the Daubechies wavelets for noise removal is between 17 and 20. Moreover, unlike the Fourier transform, the Daubechies wavelet transform demonstrates a proficient capacity in eliminating noise from data point that lies completely outside the rest in the audio data set. Wolfram Mathematica 12.3 software package was used to complete this research. This method can be applied toconserve vintage audio recordings originally recorded in cassettes and spools.
 Keywords: Digital Signal Processing, Wavelet Transforms, Daubechies Wavelet Transform, Fourier Transforms, Noise Removal

High-frequency Noise Removal of Vintage Songs in Audio Cassettes using Signal Processing Techniques


 From 1960 to the early 2000s, the audio cassette was one of the three most used storage devices for prerecorded music, alongside gramophone records (LPs) and compact discs (CDs). A compact cassette consists of two miniature spools, between which a magnetic tape is passed and wound. The tape head in tape recorders and cassette players is used to store and play audio tracks in these magnetic tapes by converting the electric signal to a magnetic fluctuation and vice versa. The sound quality of an audio track recorded on a compact cassette is decent. However, as these cassettes are stored in the Earth’s magnetic field for longer periods, the effect from it influences magnetic particles to change their orientation and distort the recorded audio signal. As a result, high-frequency noise is generated in the audio signal, resulting in a great disturbance when listening to songs and other audio recordings on vintage cassettes.
 168Therefore, the removal of such noises while minimizing the impact on the original audio signal will make sure the listener gets to enjoy the original quality of the vintage audio recordings. Fourier transform of a given distorted audio file (in “wav” format) was obtained to identify the high-frequency noise using Wolfram Mathematica 12.3. Then a variable “m” was introduced to the program to identify magnitudes of high-frequency components and remove their effect on the original audio recording. A trial-and-error approach was taken to identify the adequate “m" value for each channel of a given audio signal. Vintage songs were used to evaluate the accuracy of the developed method in this study. It was identified that the suitable “m” value changes for each channel in each song. Therefore, it is impossible to state a constant value for the variable “m”. However, the accuracy of the output also depends on the hearing frequency of the person, and the audio equipment being used. As an application, this technique can be used to conserve vintage songs that were originally stored in compact cassettes and spools, because it allows the user to control the number of high-frequency components removed from the distorted audio signal.
 Keywords: Signal Processing Techniques, Fourier Transforms, Mathematica, Noise Removing,Audio Cassettes

Fault Size Estimation of Bearings Using Multiple Decomposition Techniques with Artificial Neural Network

A running machine generates multi-frequency vibration signals which can be captured by accelerometers. Empirical mode decomposition, wavelet decomposition, and wavelet packet decomposition are the commonly used methods to decompose the multi-frequency signal. Quick fault classification, accurate signal decomposition, and fault size detection are still a problem in machines with rotary components. In the proposed work, fault diameter in rotary part of machine is detected and classified using the machine learning methods. In the first stage, we have employed empirical mode decomposition (EMD) for high-frequency noise removal. Residue signal is obtained by removing first IMF from base signal considering first IMF as a high-frequency noisy signal, followed by wavelet decomposition. Entropy of the wavelet coefficient obtained from 3rd level decomposition of residue signal is calculated which acts as an input parameter to the machine learning techniques to determine the diameter for fault. Three different sets have been taken for inner race, outer race, and ball race correspondingly. The proposed method classifies and detects the fault diameter up to 99.5%. The proposed method can be used for different types of continuous as well as discrete wavelets.

An integrated Empirical Mode Decomposition and Butterworth filter based vehicle trajectory reconstruction method

Trajectory data serving as an essential data-source, has been widely applied in traffic flow analysis, traffic prediction and transportation management. In real situations, trajectory data is often corrupted with noises, which may introduce estimation bias and control inefficiency to intelligent transportation systems. This paper presents a novel trajectory reconstruction method which is generic for both highway and urban arterial trajectories. The reconstruction method establishes an Empirical Mode Decomposition (EMD) based Butterworth low-pass filter framework to filter the noises and simultaneously maintain physical integrity. The two-stage framework firstly applies the EMD to decompose the original trajectories into components, multiple intrinsic mode functions (IMFs), to find out the main components of different temporal-frequency characteristics. Based on that, an optimal Butterworth-filter is applied on the lower order IMFs to filter the acceleration of an unexpected high-frequency range. To test the effectiveness of our proposed method, multiple resource data-sets are applied. As results indicated that our proposed reconstruction method performs well in terms of physical trajectories integrity, high-frequency noise removal, and measurement error rejection with minimum signal distortion. Further, our method efficiently produces speed and acceleration with higher quality compared with the state-of-the-art methods.

The Impact of Torso Signal Processing on Noninvasive Electrocardiographic Imaging Reconstructions

Goal:To evaluate state-of-the-art signal processing methods for epicardial potential-based noninvasive electrocardiographic imaging reconstructions of single-site pacing data.Methods:Experimental data were obtained from two torso-tank setups in which Langendorff-perfused hearts (n = 4) were suspended and potentials recorded simultaneously from torso and epicardial surfaces. 49 different signal processing methods were applied to torso potentials, grouped as i) high-frequency noise removal (HFR) methods ii) baseline drift removal (BDR) methods and iii) combined HFR+BDR. The inverse problem was solved and reconstructed electrograms and activation maps compared to those directly recorded.Results:HFR showed no difference compared to not filtering in terms of absolute differences in reconstructed electrogram amplitudes nor median correlation in QRS waveforms (p > 0.05). However, correlation and mean absolute error of activation times and pacing site localization were improved with all methods except a notch filter. HFR applied post-reconstruction produced no differences compared to pre-reconstruction. BDR and BDR+HFR significantly improved absolute and relative difference, and correlation in electrograms (p < 0.05). While BDR+HFR combined improved activation time and pacing site detection, BDR alone produced significantly lower correlation and higher localization errors (p < 0.05).Conclusion:BDR improves reconstructed electrogram morphologies and amplitudes due to a reduction in lambda value selected for the inverse problem. The simplest method (resetting the isoelectric point) is sufficient to see these improvements. HFR does not impact electrogram accuracy, but does impact post-processing to extract features such as activation times. Removal of line noise is insufficient to see these changes. HFR should be applied post-reconstruction to ensure over-filtering does not occur.

Bearing Fault Online Identification Based on ANFIS

Effectiveness of online bearing status monitoring (OBSM) depends deeply on the online data processing ability and the sensitivity of data features used to recognize the mechanical-system dynamic response change. Focusing on these, we present a novel method of OBSM based on singular spectrum analysis (SSA) and adaptive neuro-fuzzy inference system (ANFIS) with the highlights as follows. A sensitive and stable multi-feature is discovered to better the ability to distill the valuable information in noisy and massive databases (NMDs) and process impulse-noise in them. The SSA-based high-frequency noise removal solution, the ANFIS’ interpolating and identifying capability, and the dual function of the proposed multi-feature are combined in a new algorithm named AfOBSM for building a system of OBSM through two phases, offline and online. The offline is to identify the mechanical-system in the presence of the typical kinds of bearing faults. The ANFIS is trained in this phase using a training dataset. Meanwhile, the online is to estimate online the real status of the bearing(s) based on the trained ANFIS and a monitoring dataset. Surveys from an experimental-system were performed. The obtained results showed the positive effects of the AfOBSM.

Optical Dual Laser Based Sensor Denoising for OnlineMetal Sheet Flatness Measurement Using Hermite Interpolation.

Flatness sensors are required for quality control of metal sheets obtained from steel coils by roller leveling and cutting systems. This article presents an innovative system for real-time robust surface estimation of flattened metal sheets composed of two line lasers and a conventional 2D camera. Laser plane triangulation is used for surface height retrieval along virtual surface fibers. The dual laser allows instantaneous robust and quick estimation of the fiber height derivatives. Hermite cubic interpolation along the fibers allows real-time surface estimation and high frequency noise removal. Noise sources are the vibrations induced in the sheet by its movements during the process and some mechanical events, such as cutting into separate pieces. The system is validated on synthetic surfaces that simulate the most critical noise sources and on real data obtained from the installation of the sensor in an actual steel mill. In the comparison with conventional filtering methods, we achieve at least a 41% of improvement in the accuracy of the surface reconstruction.

Effects of ECG Signal Processing on the Inverse Problem of Electrocardiography.

The inverse problem of electrocardiography is ill-posed. Errors in the model such as signal noise can impact the accuracy of reconstructed cardiac electrical activity. It is currently not known how sensitive the inverse problem is to signal processing techniques. To evaluate this, experimental data from a Langendorff-perfused pig heart (n=1) suspended in a human-shaped torso-tank was used. Different signal processing methods were applied to torso potentials recorded from 128 electrodes embedded in the tank surface. Processing methods were divided into three categories i) high-frequency noise removal ii) baseline drift removal and iii) signal averaging, culminating in n=72 different signal sets. For each signal set, the inverse problem was solved and reconstructed signals were compared to those directly recorded by the sock around the heart. ECG signal processing methods had a dramatic effect on reconstruction accuracy. In particular, removal of baseline drift significantly impacts the magnitude of reconstructed electrograms, while the presence of high-frequency noise impacts the activation time derived from these signals (p<0.05).

Simultaneous monitoring of motion ECG of two subjects using Bluetooth Piconet and baseline drift.

Uninterrupted monitoring of multiple subjects is required for mass causality events, in hospital environment or for sports by medical technicians or physicians. Movement of subjects under monitoring requires such system to be wireless, sometimes demands multiple transmitters and a receiver as a base station and monitored parameter must not be corrupted by any noise before further diagnosis. A Bluetooth Piconet network is visualized, where each subject carries a Bluetooth transmitter module that acquires vital sign continuously and relays to Bluetooth enabled device where, further signal processing is done. In this paper, a wireless network is realized to capture ECG of two subjects performing different activities like cycling, jogging, staircase climbing at 100Hz frequency using prototyped Bluetooth module. The paper demonstrates removal of baseline drift using Fast Fourier Transform and Inverse Fast Fourier Transform and removal of high frequency noise using moving average and S-Golay algorithm. Experimental results highlight the efficacy of the proposed work to monitor any vital sign parameters of multiple subjects simultaneously. The importance of removing baseline drift before high frequency noise removal is shown using experimental results. It is possible to use Bluetooth Piconet frame work to capture ECG simultaneously for more than two subjects. For the applications where there will be larger body movement, baseline drift removal is a major concern and hence along with wireless transmission issues, baseline drift removal before high frequency noise removal is necessary for further feature extraction.

A topological approach to delineation and arrhythmic beats detection in unprocessed long-term ECG signals

Background and objectiveArrhythmias are one of the most common symptoms of cardiac failure. They are usually diagnosed using ECG recordings, particularly long ambulatory recordings (AECG). These recordings are tedious to interpret by humans due to their extent (up to 48 h) and the relative scarcity of arrhythmia events. This makes automated systems for detecting various AECG anomalies indispensable. In this work we present a novel procedure based on topological principles (Morse theory) for detecting arrhythmic beats in AECG. It works in nearly real-time (delayed by a 14 s window), and can be applied to raw (unprocessed) ECG signals. MethodsThe procedure is based on a subject-specific adaptation of the one-dimensional discrete Morse theory (ADMT), which represents the signal as a sequence of its most important extrema. The ADMT algorithm is applied twice; for low-amplitude, high-frequency noise removal, and for detection of the characteristic waves of individual ECG beats. The waves are annotated using the ADMT algorithm and template matching. The annotated beats are then compared to the adjacent beats with two measures of similarity: the distance between two beats, and the difference in shape between them. The two measures of similarity are used as inputs to a decision tree algorithm that classifies the beats as normal or abnormal. The classification performance is evaluated with the leave-one-record-out cross-validation method. ResultsOur approach was tested on the MIT-BIH database, where it exhibited a classification accuracy of 92.73%, a sensitivity of 73.35%, a specificity of 96.70%, a positive predictive value of 88.01%, and a negative predictive value of 95.73%. ConclusionsCompared to related studies, our algorithm requires less preprocessing while retaining the capability to detect and classify beats in almost real-time. The algorithm exhibits a high degree of accuracy in beats detection and classification that are at least comparable to state-of-the-art methods.

Electrocardiogram signal denoising based on empirical mode decomposition technique: an overview

Electrocardiogram (ECG) signal is nonlinear and non-stationary weak signal which reflects whether the heart is functioning normally or abnormally. ECG signal is susceptible to various kinds of noises such as high/low frequency noises, powerline interference and baseline wander. Hence, the removal of noises from ECG signal becomes a vital link in the ECG signal processing and plays a significant role in the detection and diagnosis of heart diseases. The review will describe the recent developments of ECG signal denoising based on Empirical Mode Decomposition (EMD) technique including high frequency noise removal, powerline interference separation, baseline wander correction, the combining of EMD and Other Methods, EEMD technique. EMD technique is a quite potential and prospective but not perfect method in the application of processing nonlinear and non-stationary signal like ECG signal. The EMD combined with other algorithms is a good solution to improve the performance of noise cancellation. The pros and cons of EMD technique in ECG signal denoising are discussed in detail. Finally, the future work and challenges in ECG signal denoising based on EMD technique are clarified.

Aeromagnetic compensation with partial least square regression

Magnetic exploration plays a significant role in regional geological investigation and detection of underground geological bodies with magnetic anomaly. At the moment, aeromagnetic survey is widely applied for its high efficiency, low cost and less subject to terrain restrict. Magnetic compensation is a key step in pre-processing survey data and several methods have been used. In this paper, we would use partial least square method to complete aeromagnetic compensation. Partial least square regression is frequently used to find the fundamental relations between two matrices. It combines linear regression analysis, canonical correlation analysis and principal components analysis. It can be applied into data with multicollinearity among independent variables and the number of variables is larger than that of observations. Before compensation, we should have several pre-processing steps such as parallax correction, diurnal variations correction, geomagnetic field correction and high-frequency noise removal. This will provide us magnetic data with higher quality and make compensation process more accurate. We set synthetic aeromagnetic data with interference of aircraft’s maneuvers and used partial least square method to do compensation. From the results of simulation, we can see that the interference signal is reduced to a low degree and satisfied compensation effect is obtained. Partial least square regression is a stable and effective method in the application of aeromagnetic compensation.

Removal of High Frequency Noise from the ECG Signal Using Averaging Filters

Removal of High Frequency Noise from the ECG Signal Using Averaging Filters

Facies recognition using a smoothing process through Fast Independent Component Analysis and Discrete Cosine Transform

We propose a preprocessing methodology for well-log geophysical data based on Fast Independent Component Analysis (FastICA) and Discrete Cosine Transform (DCT), in order to improve the success rate of the K-NN automatic classifier. The K-NN have been commonly applied to facies recognition in well-log geophysical data for hydrocarbon reservoir modeling and characterization.The preprocess was made in two different levels. In the first level, a FastICA based dimenstion reduction was applied, maintaining much of the information, and its results were classified; In second level, FastICA and DCT were applied in smoothing level, where the data points are modified, so individual points have their distance reduced, keeping just the primordial information. The results were compared to identify the best classification cases. We have applied the proposed methodology to well-log data from a petroleum field of Campos Basin, Brazil. Sonic, gamma-ray, density, neutron porosity and deep induction logs were preprocessed with FastICA and DCT, and the product was classified with K-NN. The success rates in recognition were calculated by appling the method to log intervals where core data were available. The results were compared to those of automatic recognition of the original well-log data set with and without the removal of high frequency noise. We conclude that the application of the proposed methodology significantly improves the success rate of facies recognition by K-NN.

Glacial isostatic adjustment in Fennoscandia from GRACE data and comparison with geodynamical models

Abstract The Earth’s gravity field observed by the Gravity Recovery and Climate Experiment (GRACE) satellite mission shows variations due to the integral effect of mass variations in the atmosphere, hydrosphere and geosphere. Several institutions, such as the GeoForschungsZentrum (GFZ) Potsdam, the University of Texas at Austin, Center for Space Research (CSR) and the Jet Propulsion Laboratory (JPL), Pasadena, provide GRACE monthly solutions, which differ slightly due to the application of different reduction models and centre-specific processing schemes. The GRACE data are used to investigate the mass variations in Fennoscandia, an area which is strongly influenced by glacial isostatic adjustment (GIA). Hence the focus is set on the computation of secular trends. Different filters (e.g. isotropic and non-isotropic filters) are discussed for the removal of high frequency noise to permit the extraction of the GIA signal. The resulting GRACE based mass variations are compared to global hydrology models (WGHM, LaDWorld) in order to (a) separate possible hydrological signals and (b) validate the hydrology models with regard to long period and secular components. In addition, a pattern matching algorithm is applied to localise the uplift centre, and finally the GRACE signal is compared with the results from a geodynamical modelling. The GRACE data clearly show temporal gravity variations in Fennoscandia. The secular variations are in good agreement with former studies and other independent data. The uplift centre is located over the Bothnian Bay, and the whole uplift area comprises the Scandinavian Peninsula and Finland. The secular variations derived from the GFZ, CSR and JPL monthly solutions differ up to 20%, which is not statistically significant, and the largest signal of about 1.2 μ Gal/year is obtained from the GFZ solution. Besides the GIA signal, two peaks with positive trend values of about 0.8 μ Gal/year exist in central eastern Europe, which are not GIA-induced, and also not explainable by the hydrology models. This may indicate that the recent global hydrology models have to be revised with respect to long period and secular components. Finally, the GRACE uplift signal is also in quite good agreement with the results from a simple geodynamical modelling.

A WAVELET TRANSFORM AND ITS APPLICATION TO SPECTROSCOPIC ANALYSIS

ABSTRACT An introduction to the wavelet transform and its applications in spectroscopy analysis are presented. Owing to the property of the dual localization both in time and in frequency domains, the wavelet transform exhibits several useful characteristics. With the wavelet transform a signal can be decomposed into series of contributions according to the frequency difference, these contributions are respectively called discrete approximations and discrete details. By applying certain treatment to the discrete approximations (the low frequency part) or the discrete details (the high frequency part), a part of the original signal within a certain frequency range can be obtained. The wavelet transform has been applied in various fields of the analytical chemistry, including removal of high frequency noise, information extraction, resolution of overlapping signals, and data compression etc. Research concerning application in analytical chemistry are introduced in respect of photoacoustic spectroscopy, EXAFS spectrum, NMR analysis, and Raman spectrum.