scholarly journals Signal Recognition for English Speech Translation Based on Improved Wavelet Denoising Method

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zhuo Chen
Keyword(s):  
Speech Recognition ◽  
Recognition Accuracy ◽  
Wavelet Denoising ◽  
Simulation Software ◽  
Signal Recognition ◽  
Denoising Method ◽  
Speech Translation ◽  
Time Frequency ◽  
Traditional Algorithm ◽  
Translation Signals

The signal corresponding to English speech contains a lot of redundant information and environmental interference information, which will produce a lot of distortion in the process of English speech translation signal recognition. Based on this, a large number of studies focus on encoding and processing English speech, so as to achieve high-precision speech recognition. The traditional wavelet denoising algorithm plays an obvious role in the recognition of English speech translation signals, which mainly depends on the excellent local time-frequency domain characteristics of the wavelet signal algorithm, but the traditional wavelet signal algorithm is still difficult to select the recognition threshold, and the recognition accuracy is easy to be affected. Based on this, this paper will improve the traditional wavelet denoising algorithm, abandon the single-threshold judgment of the original traditional algorithm, innovatively adopt the combination of soft threshold and hard threshold, further solve the distortion problem of the denoising algorithm in the process of English speech translation signal recognition, improve the signal-to-noise ratio of English speech recognition, and further reduce the root mean square error of the signal. Good noise reduction effect is realized, and the accuracy of speech recognition is improved. In the experiment, the algorithm is compared with the traditional algorithm based on MATLAB simulation software. The simulation results are consistent with the actual theoretical results. At the same time, the algorithm proposed in this paper has obvious advantages in the recognition accuracy of English speech translation signals, which reflects the superiority and practical value of the algorithm.

Denoising Vibration Signals of Food Refrigerant Air Compressor Based on Wavelet Transform

2010 ◽  
Vol 439-440 ◽  
pp. 1037-1041 ◽  
Author(s):  
Yan Jue Gong ◽  
Zhao Fu ◽  
Hui Yu Xiang ◽  
Li Zhang ◽  
Chun Ling Meng
Keyword(s):  
Wavelet Transform ◽  
Frequency Characteristic ◽  
Vibration Signal ◽  
Wavelet Denoising ◽  
Signal Denoising ◽  
Denoising Method ◽  
Vibration Signals ◽  
Air Compressor ◽  
Time Frequency ◽  
Soft Threshold

On the basis of wavelet denoising and its better time-frequency characteristic, this paper presents an effective vibration signal denoising method for food refrigerant air compressor. The solution of eliminating strong noise is investigated with the combination of soft threshold and exponential lipschitza. The good denoising results show that the presented method is effective for improving the signal noise ratio and builds the good foundation for further extraction of the vibration signals.

WAVELET-BASED DENOISING ALGORITHM FOR ROBUST EMG PATTERN RECOGNITION

2011 ◽  
Vol 10 (02) ◽  
pp. 157-167 ◽  
Author(s):  
ANGKOON PHINYOMARK ◽  
PORNCHAI PHUKPATTARANONT ◽  
CHUSAK LIMSAKUL
Keyword(s):  
Recognition Accuracy ◽  
Signal To Noise Ratio ◽  
Wavelet Denoising ◽  
Hand Movements ◽  
Signal Recognition ◽  
Signal To Noise ◽  
Processing Stage ◽  
Emg Signal ◽  
Baseline System ◽  
Emg Pattern Recognition

A successful pre-processing stage based on wavelet denoising algorithm for electromyography (EMG) signal recognition is proposed. From the limitation of traditional universal wavelet denoising, the optimal weighted parameter is assigned for universal thresholding method. The optimal weight for increasing EMG recognition accuracy is 50–60% of traditional universal threshold with hard transformation. Experimental results show that it improved approximately from 2 to 50% of recognition accuracy for EMG with signal-to-noise ratio (SNR) in the range of 20 to 0 dB compared to a baseline system (without pre-processing stage) and traditional universal wavelet denoising. The results are evaluated through a large EMG dataset with seven kinds of hand movements and eight types of muscle positions.

scholarly journals A Radar Signal Recognition Approach via IIF-Net Deep Learning Models

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Ji Li ◽  
Huiqiang Zhang ◽  
Jianping Ou ◽  
Wei Wang
Keyword(s):  
Recognition Accuracy ◽  
Signal To Noise Ratio ◽  
Recognition Rate ◽  
Software Radio ◽  
Radar Signal ◽  
Signal Recognition ◽  
Electromagnetic Environment ◽  
Time Frequency ◽  
Low Snr ◽  
Radar Signals

In the increasingly complex electromagnetic environment of modern battlefields, how to quickly and accurately identify radar signals is a hotspot in the field of electronic countermeasures. In this paper, USRP N210, USRP-LW N210, and other general software radio peripherals are used to simulate the transmitting and receiving process of radar signals, and a total of 8 radar signals, namely, Barker, Frank, chaotic, P1, P2, P3, P4, and OFDM, are produced. The signal obtains time-frequency images (TFIs) through the Choi–Williams distribution function (CWD). According to the characteristics of the radar signal TFI, a global feature balance extraction module (GFBE) is designed. Then, a new IIF-Net convolutional neural network with fewer network parameters and less computation cost has been proposed. The signal-to-noise ratio (SNR) range is −10 to 6 dB in the experiments. The experiments show that when the SNR is higher than −2 dB, the signal recognition rate of IIF-Net is as high as 99.74%, and the signal recognition accuracy is still 92.36% when the SNR is −10 dB. Compared with other methods, IIF-Net has higher recognition rate and better robustness under low SNR.

State Monitoring of Lathes Based on Load Current Signal

2012 ◽  
Vol 201-202 ◽  
pp. 758-762
Author(s):  
Yue Ping Yu ◽  
Guang Lin Yu ◽  
Hong Bin Li ◽  
Guo Fu Li
Keyword(s):  
Wavelet Packet ◽  
Wavelet Denoising ◽  
Wavelet Packet Transform ◽  
Current Signal ◽  
State Monitoring ◽  
Denoising Method ◽  
Load Current ◽  
Time Frequency ◽  
Characteristic Quantity ◽  
Motor Load

According to the characteristics of machine tools such as complex driving chain ,weak signal and enclosed housing,this paper takes horizontal lathes as study objects and selects current signal which is easy to sample as the analytical signal.We collect motor load current signals of idling, cylindrical cutting and end cutting processing state in the experiment to process the condition monitoring based on wavelet denoising and wavelet packet transform. We take advantage of the threshold denoising method to reduce noise of load current signal.Then we use time-frequency analysis methods of wavelet packet transform to extract state characteristic quantity and outstand useful information.So in this paper we monitor the working state of lathes based on the unique advantages of wavelet denoising and wavelet packet transform, and this method can be widely used in various fields of state monitoring.

Research of Image Denoising Method about Wavelet Transform with Neighborhood Average

2014 ◽  
Vol 989-994 ◽  
pp. 4054-4057 ◽  
Author(s):  
Chen Huang
Keyword(s):  
Wavelet Transform ◽  
Image Denoising ◽  
Wavelet Denoising ◽  
Good Time ◽  
Threshold Selection ◽  
Threshold Method ◽  
Denoising Method ◽  
Time Frequency ◽  
Image Details ◽  
Denoised Image

Because wavelet transform has good time-frequency characteristics, and its application in image denoising has been promising. Firstly, use the threshold method of the wavelet transform is used in removing image noise, and then the denoised image is smoothed using neighborhood average filtering with Gauss template. And wavelet denoising process and domain threshold selection principle are discussed. Simulation results show that this method can effectively reduce the noise and can remain most of image details better.

scholarly journals A Local Search Maximum Likelihood Parameter Estimator of Chirp Signal

2021 ◽  
Vol 11 (2) ◽  
pp. 673
Author(s):  
Guangli Ben ◽  
Xifeng Zheng ◽  
Yongcheng Wang ◽  
Ning Zhang ◽  
Xin Zhang
Keyword(s):  
Maximum Likelihood ◽  
Local Search ◽  
Signal To Noise Ratio ◽  
Estimation Accuracy ◽  
Chirp Signal ◽  
Estimation Of Parameters ◽  
Parameter Estimator ◽  
Denoising Method ◽  
Time Frequency ◽  
Approximate Estimation

A local search Maximum Likelihood (ML) parameter estimator for mono-component chirp signal in low Signal-to-Noise Ratio (SNR) conditions is proposed in this paper. The approach combines a deep learning denoising method with a two-step parameter estimator. The denoiser utilizes residual learning assisted Denoising Convolutional Neural Network (DnCNN) to recover the structured signal component, which is used to denoise the original observations. Following the denoising step, we employ a coarse parameter estimator, which is based on the Time-Frequency (TF) distribution, to the denoised signal for approximate estimation of parameters. Then around the coarse results, we do a local search by using the ML technique to achieve fine estimation. Numerical results show that the proposed approach outperforms several methods in terms of parameter estimation accuracy and efficiency.

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