Empirical mode decomposition based reconstruction of speech signal in noisy environment

2014 ◽  
Author(s):  
Nisha Goswami ◽  
Mousmita Sarma ◽  
Kandarpa Kumar Sarma
Keyword(s):  
Empirical Mode Decomposition ◽  
Speech Signal ◽  
Noisy Environment ◽  
Mode Decomposition

scholarly journals Speech Endpoint Detection in Noisy Environment Based on the Ensemble Empirical Mode Decomposition

2011 ◽  
Vol 2-3 ◽  
pp. 135-139
Author(s):  
Jing Jiao Li ◽  
Dong An ◽  
Jiao Wang ◽  
Chao Qun Rong
Keyword(s):  
Empirical Mode Decomposition ◽  
Speech Signal ◽  
Recognition System ◽  
Ensemble Empirical Mode Decomposition ◽  
Endpoint Detection ◽  
Noisy Environment ◽  
Low Snr ◽  
Voice Signal ◽  
Mode Decomposition ◽  
Speech Endpoint Detection

Speech endpoint detection is one of the key problems in the practical application of speech recognition system. In this paper, speech signal contained chirp is decomposed into several intrinsic mode function (IMF) with the method of ensemble empirical mode decomposition (EEMD). At the same time, it eliminates the modal mix superposition phenomenon which usually comes out in processing speech signal with the algorithm of empirical mode decomposition (EMD). After that, selects IMFs contained major noise through the adaptive algorithm. Finally, the IMFs and speech signal contained chirp are input into the independent component analysis (ICA) and pure voice signal is separated out. The accuracy of speech endpoint detection can be improved in this way. The result shows that the new speech endpoint detection method proposed above is effective, and has strong anti-noises ability, especially suitable for the speech endpoint detection in low SNR.

scholarly journals Correction to: Emotion classification from speech signal based on empirical mode decomposition and non-linear features

2021 ◽  
Author(s):  
Palani Thanaraj Krishnan ◽  
Alex Noel Joseph Raj ◽  
Vijayarajan Rajangam
Keyword(s):  
Empirical Mode Decomposition ◽  
Speech Signal ◽  
Emotion Classification ◽  
Linear Features ◽  
Mode Decomposition ◽  
Non Linear

A correction to this paper has been published: https://doi.org/10.1007/s40747-021-00377-y

EEMD-Based Speaker Emotional Analysis for Speech Signal

2011 ◽  
Vol 121-126 ◽  
pp. 815-819 ◽  
Author(s):  
Yu Qiang Qin ◽  
Xue Ying Zhang
Keyword(s):  
Emotion Recognition ◽  
Empirical Mode Decomposition ◽  
Speech Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
New Method ◽  
Speech Signals ◽  
Emotional Speech ◽  
Mode Decomposition ◽  
Two Parameters ◽  
Mode Mixing

Ensemble empirical mode decomposition(EEMD) is a newly developed method aimed at eliminating mode mixing present in the original empirical mode decomposition (EMD). To evaluate the performance of this new method, this paper investigates the effect of two parameters pertinent to EEMD: the emotional envelop and the number of emotional ensemble trials. At the same time, the proposed technique has been utilized for four kinds of emotional(angry、happy、sad and neutral) speech signals, and compute the number of each emotional ensemble trials. We obtain an emotional envelope by transforming the IMFe of emotional speech signals, and obtain a new method of emotion recognition according to different emotional envelop and emotional ensemble trials.

Pathological speech signal analysis and classification using empirical mode decomposition

2013 ◽  
Vol 51 (7) ◽  
pp. 811-821 ◽  
Author(s):  
Muhammad Kaleem ◽  
Behnaz Ghoraani ◽  
Aziz Guergachi ◽  
Sridhar Krishnan
Keyword(s):  
Empirical Mode Decomposition ◽  
Speech Signal ◽  
Signal Analysis ◽  
Mode Decomposition

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.

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.

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