Wavelet speech enhancement algorithm using exponential semi-soft mask filtering

Lee, Gihyoun; Na, Sung Dae; Seong, Ki Woong; Cho, Jin‐Ho; Kim, Myoung Nam

doi:10.1080/21655979.2016.1197617

Cited by 7 publications

(6 citation statements)

References 9 publications

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“…In addition, all audio signals used in the experiments are sampled at 8 kHz and a 128 samples Hamming window with a 50% overlap is used to compute the DFT coefficients. The proposed algorithm is also compared with the classic prior SNR‐based Wiener filter algorithm [2], the audible noise suppression algorithm [21], the acoustic mask threshold constrained algorithm proposed in [24], and the mask filtering‐based algorithm [26].…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…The higher score indicates the better intelligibility of the enhanced speech. After making the pairwise comparison through the scores of five algorithms ( [2,21,24,26] and the proposed algorithm) with the t-test technique, the final subjective intelligibility test results are obtained. When the input SNR ranges from 0 to 5 dB, the t-test results from Tables 3-5 are the same, the test statistic t of means between the proposed method and other comparing methods are >2.262.…”

Section: Subjective Evaluation Of the Enhanced Speechmentioning

confidence: 99%

“…In most of the speech enhancement approaches, residual noise is not completely suppressed. Thus, algorithms in [21][22][23][24][25][26][27] attempt to make the residual noise perceptually inaudible and improve the speech intelligibility by exploiting the characteristics of the human auditory system. A Wiener filter in [23] with noise shaping aims to adjust the suppressing weight based on the noise distortion spectrum to make it inaudible.…”

Section: Introductionmentioning

confidence: 99%

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Probability decision‐driven speech enhancement algorithm based on human acoustic perception

Zhang

Wang

Idrees

et al. 2020

IET signal process.

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In this study, a novel human acoustic perception motivated Wiener filter speech enhancement system is presented to cope with real-world interfering background noises. Guiding by the speech presence probability, two alternative methods are proposed to reduce the noise by adopting the audible sound pressure level (SPL) and the masking characteristic of the human auditory system to achieve better listening comfort level. More specifically, when the probability of speech presence in the noisy signal is less than the decision threshold, a new SPL compressed method effectively reduces the noise. When the speech presence probability is more than the decision threshold, an improved acoustical mask threshold constrained Wiener filter approach enhances the noisy speech. Moreover, in order to evaluate the performance of the new system, the proposed algorithm is compared with the classic prior signal-to-noise ratio-based Wiener filter and three acoustic perception related algorithms. The experimental results show that the proposed algorithm significantly outperforms the four comparing algorithms in terms of speech quality and intelligibility either in stationary or moderate non-stationary noisy environments. Thus, the intended approach can be employed as the front-end module for various speech-related applications.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Subjective Evaluation Of the Enhanced Speechmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Probability decision‐driven speech enhancement algorithm based on human acoustic perception

Zhang

Wang

Idrees

et al. 2020

IET signal process.

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show abstract

“…It is possible to resolve high-frequency components within a small time window of the signal [ 32 ]. Generally, WT is employed to decompose a signal by transforming a wavelet packet into time–frequency wavelet coefficients of multiple sub-bands [ 33 , 34 ]. In this study, WT decomposition was designed to represent the time–frequency form of the fNIRS signal using the Daubechies6 wavelet basis.…”

Section: Methodsmentioning

confidence: 99%

Motion Artifact Correction of Multi-Measured Functional Near-Infrared Spectroscopy Signals Based on Signal Reconstruction Using an Artificial Neural Network

Lee

Jin

2018

Sensors

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In this paper, a new motion artifact correction method is proposed based on multi-channel functional near-infrared spectroscopy (fNIRS) signals. Recently, wavelet transform and hemodynamic response function-based algorithms were proposed as methods of denoising and detrending fNIRS signals. However, these techniques cannot achieve impressive performance in the experimental environment with lots of movement such as gait and rehabilitation tasks because hemodynamic responses have features similar to those of motion artifacts. Moreover, it is difficult to correct motion artifacts in multi-measured fNIRS systems, which have multiple channels and different noise features in each channel. Thus, a new motion artifact correction method for multi-measured fNIRS is proposed in this study, which includes a decision algorithm to determine the most contaminated fNIRS channel based on entropy and a reconstruction algorithm to correct motion artifacts by using a wavelet-decomposed back-propagation neural network. The experimental data was achieved from six subjects and the results were analyzed in comparing conventional algorithms such as HRF smoothing, wavelet denoising, and wavelet MDL. The performance of the proposed method was proven experimentally using the graphical results of the corrected fNIRS signal, CNR that is a performance evaluation index, and the brain activation map.

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“…22 In general, WPD decomposes the noisy signal into time-frequency wavelet coefficients of multiple sub-bands using the wavelet packet transform. 23 In this paper, WPD was designed to enhance an fNIRS signal based on Daubechies 6 wavelet, which is a very efficient algorithm to reconstruct at an analysis of time-frequency domain. 24 The structure of the critical bands in WPD is optimized to compartmentalize fNIRS signal bands.…”

Section: Theory and Methodsmentioning

confidence: 99%

Robust functional near infrared spectroscopy denoising using multiple wavelet shrinkage based on a hemodynamic response model

Lee

Jin

et al. 2018

Journal of Near Infrared Spectroscopy

Self Cite

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Functional near infrared spectroscopy can measure hemodynamic signals, and the results are similar to functional magnetic resonance imaging of blood-oxygen-level-dependent signals. Thus, functional near infrared spectroscopy can be employed to investigate brain activity by measuring the absorption of near infrared light through an intact skull. Recently, a general linear model, which is a standard method for functional magnetic resonance imaging, was applied to functional near infrared spectroscopy imaging analysis. However, the general linear model fails when functional near infrared spectroscopy signals retain noise, such as that caused by the subject's movement during measurement. Although wavelet-based denoising and hemodynamic response function smoothing are popular denoising methods for functional near infrared spectroscopy signals, these methods do not exhibit impressive performances for very noisy environments and a specific class of noise. Thus, this paper proposes a new denoising algorithm that uses multiple wavelet shrinkage and a multiple threshold function based on a hemodynamic response model. Through the experiments, the performance of the proposed algorithm is verified using graphic results and objective indexes, and it is compared with existing denoising algorithms.

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Wavelet speech enhancement algorithm using exponential semi-soft mask filtering

Cited by 7 publications

References 9 publications

Probability decision‐driven speech enhancement algorithm based on human acoustic perception

Probability decision‐driven speech enhancement algorithm based on human acoustic perception

Motion Artifact Correction of Multi-Measured Functional Near-Infrared Spectroscopy Signals Based on Signal Reconstruction Using an Artificial Neural Network

Robust functional near infrared spectroscopy denoising using multiple wavelet shrinkage based on a hemodynamic response model

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