Speech Stream Detection for Noisy Environments Based on Empirical Mode Decomposition

2013 ◽  
Vol 397-400 ◽  
pp. 2239-2242
Author(s):  
Qiang Tang ◽  
De Xiang Zhang ◽  
Qing Yan
Keyword(s):  
Empirical Mode Decomposition ◽  
Recognition Performance ◽  
Intrinsic Mode Functions ◽  
Noisy Environment ◽  
Spectral Entropy ◽  
Noisy Environments ◽  
New Approach ◽  
Speech Stream ◽  
Mode Decomposition ◽  
Teager Energy

A new approach for speech stream detection based on empirical mode decomposition (EMD) under a noisy environment is proposed. Accurate speech stream detection proves to significantly improve speech recognition performance under noise. The proposed algorithm relies on the Teager energy and spectral entropy characteristics of the signal to determine whether an input frame is speech or non-speech. Firstly, the noise signals can be decomposed into different numbers of sub-signals called intrinsic mode functions (IMFs) with the EMD. Then, spectral entropy is used to extract the desired feature for noisy IMF components and Teager energy is used to non-noisy IMF components. Finally, in order to show the effectiveness of the proposed method, we present examples showing that the new measure is more effective than traditional measures. The experiments show that the proposed algorithm can suppress different noise types with different SNR.

Speech Endpoint Detection Based on EMD and Spectral Entropy in Noisy Environments

2012 ◽  
Vol 229-231 ◽  
pp. 1296-1299 ◽  
Author(s):  
Yan Li Liu ◽  
De Xiang Zhang ◽  
Ming Wei Ji
Keyword(s):  
Speech Recognition ◽  
Recognition Accuracy ◽  
Endpoint Detection ◽  
Intrinsic Mode Functions ◽  
Noisy Environment ◽  
Spectral Entropy ◽  
Noisy Environments ◽  
Novel Approach ◽  
Mode Decomposition ◽  
Speech Endpoint Detection

Accurate endpoint detection is crucial for speech recognition accuracy. A novel approach that finds robust features for endpoint detection based on the empirical mode decomposition (EMD) algorithm and spectral entropy in a noisy environment is proposed. With the EMD, the noise signals can be decomposed into different numbers of sub-signals called intrinsic mode functions (IMFs), which is a zero-mean AM-FM component. Then spectral entropy can be used to extract the desired feature for IMF components. In order to show the effectiveness of the proposed method, we present examples showing that the new measure is more effective than traditional measures. The experiments show that the proposed algorithm can suppress different noise types with different SNR, and the algorithm is robust in the real signal tests.

A New Purification Method for Rotor Center’s Orbit by Using Ensemble Empirical Mode Decomposition

2014 ◽  
Vol 986-987 ◽  
pp. 801-804
Author(s):  
Wen Bin Zhang ◽  
Jia Xing Zhu ◽  
Ya Song Pu ◽  
Yan Ping Su
Keyword(s):  
Prior Knowledge ◽  
Empirical Mode Decomposition ◽  
Simple Algorithm ◽  
Ensemble Empirical Mode Decomposition ◽  
Rotating Machinery ◽  
Intrinsic Mode Functions ◽  
Purification Method ◽  
New Approach ◽  
Mode Decomposition ◽  
Useful Signal

. Aiming at the purification of rotor center’s orbit, a new approach was presented by using ensemble empirical mode decomposition (EEMD). Ensemble empirical mode decomposition decomposed a complicated signal into a series of intrinsic mode functions (IMFs). Then according to prior knowledge of rotating machinery, chose some interested IMFs and reconstructed the needed signal. By doing this the noises would be eliminated successfully. At last the purification of rotor center’s orbit was obtained by extracting the useful signal component. Simulation and practical results show the advantage of EEMD in noise de-noising and purification of rotor center’s orbit. This method also has simple algorithm and high calculating speed; it provides a new way for purification of rotor center’s orbit of rotating machinery.

Empirical Mode Decomposition and Robust Pitch Detection Based on Recurrence Analysis

2013 ◽  
Vol 303-306 ◽  
pp. 1035-1038
Author(s):  
Jing Fang Wang
Keyword(s):  
Empirical Mode Decomposition ◽  
Pitch Detection ◽  
Intrinsic Mode Functions ◽  
Noisy Environment ◽  
Method Performance ◽  
Recurrence Analysis ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Band Filter ◽  
Better Than

A new pitch detection method is designed by the recurrence analysis in this paper, which is combined of Empirical Mode Decomposition (EMD) and Elliptic Filter (EF). The Empirical Mode Decomposition (EMD) of Hilbert-Huang Transform (HHT) are utilized tosolve the problem, and a noisy voice is first filtered on the elliptic band filter. The two Intrinsic Mode Functions (IMF) are synthesized by EMD with maximum correlation of voice, and then the pitch be easily divided. The results show that the new method performance is better than the conventional autocorrelation algorithm and cepstrum method, especially in the part that the surd and the sonant are not evident, and get a high robustness in noisy environment.

Empirical Mode Decomposition — Window Fractal (EMDWF) Algorithm in Classification of Fingerprint of Medicinal Herbs

2017 ◽  
Vol 31 (12) ◽  
pp. 1756017 ◽  
Author(s):  
Jianwei Du ◽  
Zhengguang Xu ◽  
Zhichun Mu ◽  
Patrick Shen-Pei Wang ◽  
Yuan Yan Tang ◽  
...  
Keyword(s):  
Fractal Dimension ◽  
Empirical Mode Decomposition ◽  
Signal Sequence ◽  
Medicinal Herbs ◽  
Medicinal Herb ◽  
Intrinsic Mode Functions ◽  
New Approach ◽  
Mode Decomposition ◽  
Training Samples

This paper presents a new approach called the empirical mode decomposition — window fractal (EMDWF) algorithm in classification of fingerprint of medicinal herbs. In this way, we consider a glycyrrhiza fingerprint of medicinal herb as a signal sequence, and apply empirical mode decomposition (EMD) and Hiaguchis fractal dimension to construct a feature vector. By using EMD, the glycyrrhiza fingerprint of medicinal herb can be decomposed into some intrinsic mode functions (IMFs). As window fractal dimension (WFD) is applied to each IMF and original signal, the features of the glycyrrhiza fingerprint of medicinal herb can be obtained. Thereafter, SVM is applied as a classifier. The results of the experiments state clearly that the feature extracted by EMDWF is better than that of the existing methods including the pure EMD. With the increase of the number of training samples and the increase of the number of layers in EMD, the classification result achieves more stability.

scholarly journals Gait Recognition for Human-Exoskeleton System in Locomotion Based on Ensemble Empirical Mode Decomposition

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jing Qiu ◽  
Huxian Liu
Keyword(s):  
Empirical Mode Decomposition ◽  
Recognition Performance ◽  
Human Subjects ◽  
Gait Recognition ◽  
Ensemble Empirical Mode Decomposition ◽  
Machine Learning Algorithms ◽  
Human Gait ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Phase Recognition

As exoskeleton robots are more frequently applied to impaired people to regain mobility, detection and recognition of human gait motions is important to prepare suitable control modes for exoskeletons. This paper proposes to explore the potential of the ensemble empirical mode decomposition (EEMD) method to help analyze and recognize gait motions for human subjects who wear the exoskeleton to walk. The intrinsic mode functions (IMFs) extracted from the original gait signals by EEMD are utilized to act as inputs for classification algorithms. Evident correlations are found between some IMFs and original gait kinematic sequences. Experimental results on gait phase recognition performance on 14 able-bodied subjects are shown. The performance of the composing signals extracted from the original signals as IMF 1 ∼ IMF 8 is investigated, which indicates that IMF 8 might be helpful when wearing exoskeleton and IMF 5 might be helpful when walking without exoskeleton on gait recognition. And the similarity of joint synergy between wearing and without wearing exoskeleton is analyzed, and the result shows that the joint synergy might change between with and without wearing exoskeleton. The quantitative results show that based on some IMFs of the same orders, these machine learning algorithms can achieve promising performances.

scholarly journals Empirical Mode Decomposition Based Multi-Modal Activity Recognition

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6055
Author(s):  
Lingyue Hu ◽  
Kailong Zhao ◽  
Xueling Zhou ◽  
Bingo Wing-Kuen Ling ◽  
Guozhao Liao
Keyword(s):  
Empirical Mode Decomposition ◽  
Activity Recognition ◽  
Recognition Performance ◽  
Single Type ◽  
After School ◽  
Infinite Impulse Response ◽  
Intrinsic Mode Functions ◽  
Brain Wave ◽  
Time Frequency ◽  
Mode Decomposition

This paper aims to develop an activity recognition algorithm to allow parents to monitor their children at home after school. A common method used to analyze electroencephalograms is to use infinite impulse response filters to decompose the electroencephalograms into various brain wave components. However, nonlinear phase distortions will be introduced by these filters. To address this issue, this paper applies empirical mode decomposition to decompose the electroencephalograms into various intrinsic mode functions and categorize them into four groups. In addition, common features used to analyze electroencephalograms are energy and entropy. However, because there are only two features, the available information is limited. To address this issue, this paper extracts 11 different physical quantities from each group of intrinsic mode functions, and these are employed as the features. Finally, this paper uses the random forest to perform activity recognition. It is worth noting that the conventional approach for performing activity recognition is based on a single type of signal, which limits the recognition performance. In this paper, a multi-modal system based on electroencephalograms, image sequences, and motion signals is used for activity recognition. The numerical simulation results show that the percentage accuracies based on three types of signal are higher than those based on two types of signal or the individual signals. This demonstrates the advantages of using the multi-modal approach for activity recognition. In addition, our proposed empirical mode decomposition-based method outperforms the conventional filtering-based method. This demonstrates the advantages of using the nonlinear and adaptive time frequency approach for activity recognition.

Multifractal characterization of mechanical vibration signals through improved empirical mode decomposition-based detrended fluctuation analysis

2016 ◽  
Vol 231 (22) ◽  
pp. 4139-4149 ◽  
Author(s):  
Du Wenliao ◽  
Guo Zhiqiang ◽  
Gong Xiaoyun ◽  
Xie Guizhong ◽  
Wang Liangwen ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Detrended Fluctuation Analysis ◽  
Fluctuation Analysis ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Mode Decomposition ◽  
Kolmogorov Smirnov ◽  
Detrended Fluctuation ◽  
Smirnov Test

A novel multifractal detrended fluctuation analysis based on improved empirical mode decomposition for the non-linear and non-stationary vibration signal of machinery is proposed. As the intrinsic mode functions selection and Kolmogorov–Smirnov test are utilized in the detrending procedure, the present approach is quite available for contaminated data sets. The intrinsic mode functions selection is employed to deal with the undesired intrinsic mode functions named pseudocomponents, and the two-sample Kolmogorov–Smirnov test works on each intrinsic mode function and Gaussian noise to detect the noise-like intrinsic mode functions. The proposed method is adaptive to the signal and weakens the effect of noise, which makes this approach work well for vibration signals collected from poor working conditions. We assess the performance of the proposed procedure through the classic multiplicative cascading process. For the pure simulation signal, our results agree with the theoretical results, and for the contaminated time series, the proposed method outperforms the traditional multifractal detrended fluctuation analysis methods. In addition, we analyze the vibration signals of rolling bearing with different fault types, and the presence of multifractality is confirmed.

Ensemble Empirical Mode Decomposition Based Deep Learning Model for Short-Term Wind Power Forecasting

2022 ◽  
Author(s):  
J.M. González-Sopeña
Keyword(s):  
Deep Learning ◽  
Wind Power ◽  
Empirical Mode Decomposition ◽  
Time Series Data ◽  
Ensemble Empirical Mode Decomposition ◽  
Series Data ◽  
Intrinsic Mode Functions ◽  
Wind Power Forecasting ◽  
Mode Decomposition ◽  
Power Forecasting

Abstract. In the last few years, wind power forecasting has established itself as an essential tool in the energy industry due to the increase of wind power penetration in the electric grid. This paper presents a wind power forecasting method based on ensemble empirical mode decomposition (EEMD) and deep learning. EEMD is employed to decompose wind power time series data into several intrinsic mode functions and a residual component. Afterwards, every intrinsic mode function is trained by means of a CNN-LSTM architecture. Finally, wind power forecast is obtained by adding the prediction of every component. Compared to the benchmark model, the proposed approach provides more accurate predictions for several time horizons. Furthermore, prediction intervals are modelled using quantile regression.

scholarly journals Empirical Mode Decomposition in Discrete Time Signals Denoising

2019 ◽  
Vol 16 (1) ◽  
pp. 10-13 ◽  
Author(s):  
Zoltán Germán-Salló
Keyword(s):  
Empirical Mode Decomposition ◽  
Signal To Noise Ratio ◽  
Main Idea ◽  
Discrete Wavelet ◽  
Intrinsic Mode Functions ◽  
Nonlinear Signal Processing ◽  
Mode Decomposition ◽  
Time Signals ◽  
Nonlinear Signal ◽  
Filtering Procedure

Abstract This study explores the data-driven properties of the empirical mode decomposition (EMD) for signal denoising. EMD is an acknowledged procedure which has been widely used for non-stationary and nonlinear signal processing. The main idea of the EMD method is to decompose the analyzed signal into components without using expansion functions. This is a signal dependent representation and provides intrinsic mode functions (IMFs) as components. These are analyzed, through their Hurst exponent and if they are found being noisy components they will be partially or integrally eliminated. This study presents an EMD decomposition-based filtering procedure applied to test signals, the results are evaluated through signal to noise ratio (SNR) and mean square error (MSE). The obtained results are compared with discrete wavelet transform based filtering results.

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