Speech enhancement based on combination of wiener filter and subspace filter

Xia, Yousheng; Jian-Wen, Huang

doi:10.1109/icalip.2014.7009836

Cited by 8 publications

(4 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Nilai SNR tetap digunakan sebagai parameter kualitas sinyal pada studi ini dan dapat mengontrol pengurangan derau yang lebih baik dengan memanipulasi tapis Wiener. Tapis Wiener juga digunakan untuk mendapatkan posterior sample dari unknown data dengan menggunakan pendekatan fiducial model [15]. Yadav dkk.…”

Section: Pendahuluanunclassified

Analysis of the Wiener filters application to the spectral fluctuation patterns segmentation

Melinda

Elizar

Yunidar

et al. 2020

J. Teknol. dan Sist. Komput

View full text Add to dashboard Cite

The Wiener filter is an adaptive filter which able to produce the desired estimates. Besides, this filter can also suppress noise in digital signal processing. This study aims to segment the fluctuation pattern, which results from data acquisition from a capacitive sensor with the object H2O. The fluctuation pattern to be processed is the High Fluctuation (HF) pattern by dividing the pattern into several segments according to the input frequency. It aims to see in more detail and clearly the state of each segmentation of the pattern. The results will show noise attenuation and suppression after filtering with a Wiener filter. The Signal to Noise Ratio (SNR) value will also be analyzed, which shows that the signal quality is getting better after applying the Wiener filter. Then, the analysis of the Mean Square Error (MSE) results can provide more consistent results with a smaller average error.

show abstract

Section: Pendahuluanunclassified

Analysis of the Wiener filters application to the spectral fluctuation patterns segmentation

Melinda

Elizar

Yunidar

et al. 2020

J. Teknol. dan Sist. Komput

View full text Add to dashboard Cite

show abstract

“…The additive noisy signal model is expressed in (1). Then, the observed signal y(n) is transformed into the DKTT domain as indicated in (2). In NBSE, no linear relation exists betweenX k and Y k .…”

Section: Proposed Mmse Estimatorsmentioning

confidence: 99%

“…The associate editor coordinating the review of this manuscript and approving it for publication was Manuel Rosa-Zurera. signals, restore clean speech signals, improve speech quality and intelligibility, solve the noise pollution problem, and reduce listener fatigue [1], [2]. The process of removing noise without distorting the original speech signal is a challenging task [3].…”

Section: Introductionmentioning

confidence: 99%

Speech Enhancement Algorithm Based on Super-Gaussian Modeling and Orthogonal Polynomials

et al. 2019

View full text Add to dashboard Cite

Different types of noise from the surrounding always interfere with speech and produce annoying signals for the human auditory system. To exchange speech information in a noisy environment, speech quality and intelligibility must be maintained, which is a challenging task. In most speech enhancement algorithms, the speech signal is characterized by Gaussian or super-Gaussian models, and noise is characterized by a Gaussian prior. However, these assumptions do not always hold in real-life situations, thereby negatively affecting the estimation, and eventually, the performance of the enhancement algorithm. Accordingly, this paper focuses on deriving an optimum low-distortion estimator with models that fit well with speech and noise data signals. This estimator provides minimum levels of speech distortion and residual noise with additional improvements in speech perceptual aspects via four key steps. First, a recent transform based on an orthogonal polynomial is used to transform the observation signal into a transform domain. Second, the noise classification based on feature extraction is adopted to find accurate and mutable models for noise signals. Third, two stages of nonlinear and linear estimators based on the minimum mean square error (MMSE) and new models for speech and noise are derived to estimate a clean speech signal. Finally, the estimated speech signal in the time domain is determined by considering the inverse of the orthogonal transform. The results show that the average classification accuracy of the proposed approach is 99.43%. In addition, the proposed algorithm significantly outperforms existing speech estimators in terms of quality and intelligibility measures.

show abstract

“…In the early days, single-microphone studies were conducted in noise reduction methods [1], [2] and [3]. Better re-sults were obtained from noise reduction methods performed with multiple microphones by benefiting from the correlation between signals received in the future.…”

mentioning

confidence: 99%

A Novel Voice Activity Detection for Multi-Channel Noise Reduction

Colak¹,

Akdeniz

2021

IEEE Access

View full text Add to dashboard Cite

In this study, a voice activity detection technique is designed using features such as short-term energy, periodicity and spectral flatness. The desired results are obtained by using these three features, even at low signal to noise ratio values. In addition, performance of multi-channel noise reduction algorithms such as Wiener speech distortion weighted, spatial prediction, minimum variance distortion-less response are compared using the proposed voice activity detection. Two different audio signals and three different noise types are used in the experiment. Noisy speech and only detection of noisy areas have been performed by proposed voice activity detection algorithm. The filter coefficients have been calculated for each filter algorithm used after detection of noisy speech and only noisy areas. The calculated filter coefficients have been multiplied by the frequency components of the signal received from the reference microphone to obtain an enhanced signal. Segmental signal to noise ratio, an objective method, and mean opinion score as a subjective method have been used to evaluate the performance of the filters. Speech distortion weighted Wiener filter has been found to be the best filter for noise reduction performance.

show abstract

Speech enhancement based on combination of wiener filter and subspace filter

Cited by 8 publications

References 23 publications

Analysis of the Wiener filters application to the spectral fluctuation patterns segmentation

Analysis of the Wiener filters application to the spectral fluctuation patterns segmentation

Speech Enhancement Algorithm Based on Super-Gaussian Modeling and Orthogonal Polynomials

A Novel Voice Activity Detection for Multi-Channel Noise Reduction

Contact Info

Product

Resources

About