scholarly journals Indoor Acoustic Signals Enhanced Algorithm and Visualization Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Suqing Yan ◽  
Xiaonan Luo ◽  
Xiyan Sun ◽  
Jianming Xiao ◽  
Jingyue Jiang
Keyword(s):  
Delaunay Triangulation ◽  
Acoustic Signal ◽  
Clustering Algorithm ◽  
Sound Field ◽  
Acoustic Signals ◽  
Mean Square ◽  
Least Mean Square ◽  
Fxlms Algorithm ◽  
Fcm Clustering ◽  
Auditory Fatigue

A pure acoustic signal can be easy to realize signal analysis and feature extraction. However, the surrounding noises will affect the content of acoustic signals as well as auditory fatigue to the audience. Therefore, it is vital to overcome the problem of noises that affect the acoustic signal. An indoor acoustic signal enhanced method based on image source (IS) method, filtered-x least mean square (FxLMS) algorithm, and the combination of Delaunay triangulation and fuzzy c-means (FCM) clustering algorithm is proposed. In the first stage of the proposed system, the IS method was used to simulate indoor impulse response. Next, the FxLMS algorithm was used to reduce the acoustic signals with noise. Lastly, the quiet areas are optimized and visualized by combining the Delaunay triangulation and FCM clustering algorithm. The experimental analysis results on the proposed system show that better noise reduction can be achieved than the most widely used least mean square algorithm. Visualization was validated with an intuitive understanding of the indoor sound field distribution and the quiet areas.

Hardware Implementation of Kalman Least Mean Square-Based Adaptive Algorithm for Denoising Ambient Noises in Shallow Water Region

2014 ◽  
Vol 13 (03) ◽  
pp. 1450018
Author(s):  
S. Sakthivel Murugan ◽  
V. Natarajan ◽  
S. Prethivika
Keyword(s):  
Shallow Water ◽  
Ambient Noise ◽  
Adaptive Filters ◽  
Signal To Noise Ratio ◽  
Acoustic Signals ◽  
Mean Square ◽  
Least Mean Square ◽  
Useful Signal ◽  
Custom Made ◽  
Source Signal

Signals transmitted over long distances through underwater acoustic channels are prone to corruption due to wind interference, ambient noises and various other sources of disturbance. Adaptive filters can be used to extenuate the effect of ambient noise in acoustic signals. A competent technique to denoise acoustic signals using adaptive filters has been proposed. Adaptive filtering techniques such as least mean square (LMS), normalized least mean square (NLMS) and Kalman least mean square (KLMS) have been analyzed based on their performance, with the help of characteristics like signal-to-noise ratio (SNR) and mean square error (MSE) for various wind speeds. An exhaustive set of data, collected using a custom made fixture containing two hydrophones, from shallow water regions in Bay of Bengal, have been used to verify the efficacy of this method. Based on the results obtained by simulation and Lab window simulator, hardware has been designed to denoise the useful signal. The defective source signal is passed through a Kalman filter based denoising hardware system. This system performs necessary operations to denoise the defective source signal and the final turnout is made free from ambient noise. The denoised signal is then stored in an external device for future use.

Active vibration control for the time-varying systems with a new adaptive algorithm

2019 ◽  
Vol 26 (3-4) ◽  
pp. 200-213 ◽  
Author(s):  
Hongbo Zheng ◽  
Hui Qin ◽  
Mingke Ren ◽  
Zhiyi Zhang
Keyword(s):  
Vibration Control ◽  
Adaptive Algorithm ◽  
Active Vibration Control ◽  
Superior Performance ◽  
Time Varying ◽  
Mean Square ◽  
Least Mean Square ◽  
Fxlms Algorithm ◽  
Time Varying Systems ◽  
Active Vibration

This paper proposes a new adaptive algorithm for the active vibration control of time-varying systems in the presence of broadband or narrowband disturbances. The new algorithm combines the conventional filtered-x least mean square algorithm with the recursive prediction error (RPE) algorithm after the gradient modification of the RPE algorithm. The modified RPE algorithm is used to estimate the model of the control path online. The well-known filtered-x least mean square (FxLMS) algorithm is effective for the uncertain or time-varying systems, and adopts an auxiliary white noise approach to estimate the model of the control path online. However, the auxiliary excitation will degrade the control performance to some extent. In the new algorithm, the auxiliary excitation is eliminated at the expense of a larger computational burden. The influence of the estimated finite impulse response series on the convergence is also discussed. A propulsion shafting model with the time-varying dynamics is established by frequency response function synthesis. Numerical simulation for the established model is presented to demonstrate the superior performance of the proposed algorithm as compared with the FxLMS algorithm.

scholarly journals Comparison of Performance and Computational Complexity of Nonlinear Active Noise Control Algorithms

2011 ◽  
Vol 2011 ◽  
pp. 1-9 ◽  
Author(s):  
Mouayad A. Sahib ◽  
Raja Kamil
Keyword(s):  
Computational Complexity ◽  
Noise Control ◽  
Active Noise Control ◽  
Practical Implementation ◽  
Mean Square ◽  
Least Mean Square ◽  
Computational Load ◽  
Active Noise ◽  
Nonlinear Algorithms ◽  
Fxlms Algorithm

Research on nonlinear active noise control (NANC) revolves around the investigation of the sources of nonlinearity as well as the performance and computational load of the nonlinear algorithms. The nonlinear sources could originate from the noise process, primary and secondary propagation paths, and actuators consisting of loudspeaker, microphone or amplifier. Several NANCs including Volterra filtered-x least mean square (VFXLMS), bilinear filtered-x least mean square (BFXLMS), and filtered-s least mean square (FSLMS) have been utilized to overcome these nonlinearities effects. However, the relative performance and computational complexities of these algorithm in comparison to FXLMS algorithm have not been carefully studied. In this paper, systematic comparisons of the FXLMS against the nonlinear algorithms are evaluated in overcoming various nonlinearity sources. The evaluation of the algorithms performance is standardized in terms of the normalized mean square error while the computational complexity is calculated based on the number of multiplications and additions in a single iteration. Computer simulations show that the performance of the FXLMS is more than 80% of the most effective nonlinear algorithm for each type of nonlinearity sources at the fraction of computational load. The simulation results also suggest that it is more advantageous to use FXLMS for practical implementation of NANC.

Improved least mean square adaptive filter algorithm

2013 ◽  
Vol 32 (7) ◽  
pp. 2078-2081
Author(s):  
Cheng-xi WANG ◽  
Yi-an LIU ◽  
Qiang ZHANG
Keyword(s):  
Adaptive Filter ◽  
Mean Square ◽  
Least Mean Square

scholarly journals An Effective Adaptive Combination Strategy for Distributed Learning Network

2021 ◽  
Vol 11 (12) ◽  
pp. 5723
Author(s):  
Chundong Xu ◽  
Qinglin Li ◽  
Dongwen Ying
Keyword(s):  
Minimum Variance ◽  
Iteration Scheme ◽  
Estimation Problem ◽  
Lms Algorithm ◽  
Unbiased Estimation ◽  
Mean Square ◽  
Least Mean Square ◽  
Combination Strategy ◽  
Adaptive Combination ◽  
Diffusion Networks

In this paper, we develop a modified adaptive combination strategy for the distributed estimation problem over diffusion networks. We still consider the online adaptive combiners estimation problem from the perspective of minimum variance unbiased estimation. In contrast with the classic adaptive combination strategy which exploits orthogonal projection technology, we formulate a non-constrained mean-square deviation (MSD) cost function by introducing Lagrange multipliers. Based on the Karush–Kuhn–Tucker (KKT) conditions, we derive the fixed-point iteration scheme of adaptive combiners. Illustrative simulations validate the improved transient and steady-state performance of the diffusion least-mean-square LMS algorithm incorporated with the proposed adaptive combination strategy.

Sign in / Sign up

Export Citation Format

Share Document