Dual tree complex wavelet transform based speech enhancement

Tasmaz, Haci

doi:10.1109/siu.2015.7129955

Cited by 2 publications

(1 citation statement)

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“…It also reduces aliasing. Due to these advantages of DT-CWT, it has been utilized in many applications, such as noise suppression [12][13][14][15], face recognition [16][17][18], speech enhancement [19], and image fusion [20,21]. Although DT-CWT based image fusion is distinguished by these properties, it still gives out of focus (blur) results because it uses two DWT filters.…”

Section: Introductionmentioning

confidence: 99%

Image Focus Enhancement Using Focusing Filter and DT-CWT Based Image Fusion

Habeeb

2021

eijs

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Combining multi-model images of the same scene that have different focus distances can produce clearer and sharper images with a larger depth of field. Most available image fusion algorithms are superior in results. However, they did not take into account the focus of the image. In this paper a fusion method is proposed to increase the focus of the fused image and to achieve highest quality image using the suggested focusing filter and Dual Tree-Complex Wavelet Transform. The focusing filter consist of a combination of two filters, which are Wiener filter and a sharpening filter. This filter is used before the fusion operation using Dual Tree-Complex Wavelet Transform. The common fusion rules, which are the average-fusion rule and maximum-fusion rule, were used to obtain the fused image. In the experiment, using the focus operators, the performance of the proposed fusion algorithm was compared with the existing algorithms. The results showed that the proposed method is better than these fusion methods in terms of the focus and quality.

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Section: Introductionmentioning

confidence: 99%

Image Focus Enhancement Using Focusing Filter and DT-CWT Based Image Fusion

Habeeb

2021

eijs

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Speech enhancement via two-stage dual tree complex wavelet packet transform with a speech presence probability estimator

Sun

Qin

2017

The Journal of the Acoustical Society of America

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In this paper, a two-stage dual tree complex wavelet packet transform (DTCWPT) based speech enhancement algorithm has been proposed, in which a speech presence probability (SPP) estimator and a generalized minimum mean squared error (MMSE) estimator are developed. To overcome the drawback of signal distortions caused by down sampling of wavelet packet transform (WPT), a two-stage analytic decomposition concatenating undecimated wavelet packet transform (UWPT) and decimated WPT is employed. An SPP estimator in the DTCWPT domain is derived based on a generalized Gamma distribution of speech, and Gaussian noise assumption. The validation results show that the proposed algorithm can obtain enhanced perceptual evaluation of speech quality (PESQ), and segmental signal-to-noise ratio (SegSNR) at low signal-to-noise ratio (SNR) nonstationary noise, compared with four other state-of-the-art speech enhancement algorithms, including optimally modified log-spectral amplitude (OM-LSA), soft masking using a posteriori SNR uncertainty (SMPO), a posteriori SPP based MMSE estimation (MMSE-SPP), and adaptive Bayesian wavelet thresholding (BWT).

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Dual tree complex wavelet transform based speech enhancement

Cited by 2 publications

References 0 publications

Image Focus Enhancement Using Focusing Filter and DT-CWT Based Image Fusion

Image Focus Enhancement Using Focusing Filter and DT-CWT Based Image Fusion

Speech enhancement via two-stage dual tree complex wavelet packet transform with a speech presence probability estimator

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