Application of Variational Mode Decomposition on Speech Enhancement

Enhancement of speech signal and reduction of noise from speech is still a challenging task for researchers. Out of many methods signal decomposition method attracts a lot in recent years. Empirical Mode Decomposition (EMD) has been applied in many problems of decomposition. Recently Variational Mode Decomposition (VMD) is introduced as an alternative to it that can easily separate the signals of similar frequencies. This paper proposes the signal decomposition algorithm as VMD for denoising and enhancement of speech signal. VMD decomposes the recorded speech signal into several modes. Speech contaminated with different types of noise is adaptively decomposed into various components is said to be Intrinsic Mode Functions (IMFs) by sifting process as in Empirical Mode decomposition (EMD) method. Next to it the denoising technique is applied using VMD. Each of the decomposed modes is compact. The simulation result shows that the proposed method is well suited for the speech enhancement and removal of noise by restoring the original

[1]  David Malah,et al.  Speech enhancement using a minimum mean-square error log-spectral amplitude estimator , 1984, IEEE Trans. Acoust. Speech Signal Process..

[2]  Gora Chand Nandi,et al.  Robust and accurate feature selection for humanoid push recovery and classification: deep learning approach , 2017, Neural Computing and Applications.

[3]  Yibin Hou,et al.  A fast adaptive Kalman filtering algorithm for speech enhancement , 2011, 2011 IEEE International Conference on Automation Science and Engineering.

[4]  Mojtaba Lotfizad,et al.  A Family of Adaptive Filter Algorithms in Noise Cancellation for Speech Enhancement , 2011, ArXiv.

[5]  Wu Caiyun Study of Speech Enhancement System Using Combinational Adaptive Filtering , 2012, 2012 International Conference on Computer Distributed Control and Intelligent Environmental Monitoring.

[6]  Junfeng Li,et al.  Noise reduction based on adaptive β-order generalized spectral subtraction for speech enhancement , 2007, INTERSPEECH.

[7]  S. Boll,et al.  Suppression of acoustic noise in speech using spectral subtraction , 1979 .

[8]  Abhishek Vaish,et al.  Feature-level fusion of mental task’s brain signal for an efficient identification system , 2015, Neural Computing and Applications.

[9]  Yang Lu,et al.  A geometric approach to spectral subtraction , 2008, Speech Commun..

[10]  Abhishek Vaish,et al.  Information-Theoretic Measures on Intrinsic Mode Function for the Individual Identification Using EEG Sensors , 2015, IEEE Sensors Journal.

[11]  Aurobinda Routray,et al.  Power quality disturbances classification using support vector machines with optimised time-frequency kernels , 2012 .

[12]  Vijay Bhaskar Semwal,et al.  An optimized feature selection technique based on incremental feature analysis for bio-metric gait data classification , 2017, Multimedia Tools and Applications.

[13]  Philipos C. Loizou,et al.  Speech Enhancement: Theory and Practice , 2007 .

[14]  Mihir Narayan Mohanty,et al.  Performance Analysis of Adaptive Algorithms for Speech Enhancement Applications , 2016 .

[15]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.