Acoustic measurements using sine sweeps are prone to background noise and non-stationary disturbances. Repeated measurements can be averaged to improve the resulting signal-to-noise ratio. However, averaging leads to poor rejection of non-stationary high-energy disturbances and, in the case of a time-variant environment, causes attenuation at high frequencies. This paper proposes a robust method to combine repeated sweep measurements using across-measurement median filtering in the time-frequency domain. The method, called Mosaic, successfully rejects non-stationary noise, suppresses background noise, and is more robust toward time variation than averaging. The proposed method allows high-quality measurement of impulse responses in a noisy environment.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1121/10.0028203 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Enhanced long short-term memory architectures for chaotic systems modeling: An extensive study on the Lorenz system.
Chaos
December 2024
Department of Electrical Engineering and Information Technology, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures, Târgu Mureş 540139, Romania.
Despite recent advancements in machine learning algorithms, well-established models like the Long Short-Term Memory (LSTM) are still widely used for modeling tasks. This paper introduces an enhanced LSTM variant and explores its capabilities in multiple input single output chaotic system modeling, offering a large-scale analysis that focuses on LSTM gate-level architecture, the effects of noise, non-stationary and dynamic behavior modeling, system parameter drifts, and short- and long-term forecasting. The experimental evaluation is performed on datasets generated using MATLAB, where the Lorenz and Rössler system equations are implemented and simulated in various scenarios.
View Article and Find Full Text PDFStrong Interference Elimination in Seismic Data Using Multivariate Variational Mode Extraction.
Sensors (Basel)
November 2024
Key Laboratory of Deep Petroleum Intelligent Exploration and Development, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China.
Seismic data acquired in the presence of mechanical vibrations or power facilities may be contaminated by strong interferences, significantly decreasing the data signal-to-noise ratio (S/N). Conventional methods, such as the notch filter and time-frequency transform method, are usually inadequate for suppressing non-stationary interference noises, and may distort effective signals if overprocessing. In this study, we propose a method for eliminating mechanical vibration interferences in seismic data.
View Article and Find Full Text PDFAdaptive augmented cubature Kalman filter/smoother for ECG denoising.
Biomed Eng Lett
July 2024
Department of Electrical and Computer Engineering and Advanced Technologies, Urmia University, Urmia, Iran.
Model-based Bayesian approaches have been widely applied in Electrocardiogram (ECG) signal processing, where their performances heavily rely on the accurate selection of model parameters, particularly the state and measurement noise covariance matrices. In this study, we introduce an adaptive augmented cubature Kalman filter/smoother (CKF/CKS) for ECG processing, which updates the noise covariance matrices at each time step to accommodate diverse noise types and input signal-to-noise ratios (SNRs). Additionally, we incorporate the dynamic time warping technique to enhance the filter's efficiency in the presence of heart rate variability.
View Article and Find Full Text PDFA novel nonlinear bispectrum analysis for dynamical complex oscillations.
Cogn Neurodyn
June 2024
Beijing Institute of Technology, Beijing, 100081 China.
Unlabelled: In this study, we proposed a novel set of bispectrum in constructing both frequency power and complexity spectrum. The uniform phase empirical mode decomposition (UPEMD) was implemented to obtain nonlinear extraction while guaranteeing explicit frequencies. Lepel-Ziv complexity (LZC) and frequency power per mode were used for comprehensive frequency spectra.
View Article and Find Full Text PDFDecoding Brain Signals from Rapid-Event EEG for Visual Analysis Using Deep Learning.
Sensors (Basel)
October 2024
Institute of Computer Science, Khwaja Fareed University of Engineering & Information Technology, Rahim Yar Khan 64200, Pakistan.
The perception and recognition of objects around us empower environmental interaction. Harnessing the brain's signals to achieve this objective has consistently posed difficulties. Researchers are exploring whether the poor accuracy in this field is a result of the design of the temporal stimulation (block versus rapid event) or the inherent complexity of electroencephalogram (EEG) signals.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!