Enhancement of speech signals corrupted by impulsive noise using wavelets and adaptive median filtering

Swamy, Prateek Basavapur; Hallikar, Rohini S.; Kumari, Meena

doi:10.1109/ispcc.2013.6663393

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…All signal de-noising effect highlights a complete dictionary of sparse decomposition method is significantly superior to complete dictionary of orthogonal decomposition denoising performance, fully illustrates the redundant dictionary than orthogonal dictionary is more accurate and adaptive representation of the original signal, and the absolute advantage of sparse decomposition de-noising method was verified [30][31][32][33][34]. Independent threshold value method is according to different needs, choose different from the default threshold value, then use reconstruction algorithm for de-noising reconstruction.…”

Section: The Proposed Novel De-noising Algorithmmentioning

confidence: 89%

A Novel Audio Signal De-noising Algorithm based on the Theory of Frequency Domain Analysis and Transform Domain Model

Ma¹

2017

IJFGCN

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Section: The Proposed Novel De-noising Algorithmmentioning

confidence: 89%

A Novel Audio Signal De-noising Algorithm based on the Theory of Frequency Domain Analysis and Transform Domain Model

Ma¹

2017

IJFGCN

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“…In relation to various possible locator-interpolator methods, Swamy et al [22] have proposed a potential noise cancellation technique based on wavelet transform that can remove the kinds of impulsive noise that commonly corrupts speech signals. The technique involves applying a discrete wavelet transform to a corrupted speech signal to obtain both approximate and detailed coefficients.…”

Section: Related Workmentioning

confidence: 99%

Two-Sided LPC-Based Speckle Noise Removal for Laser Speech Detection Systems

Wang

Zhang

Wang

2021

IEICE Trans. Inf. & Syst.

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Laser speech detection uses a non-contact Laser Doppler Vibrometry (LDV)-based acoustic sensor to obtain speech signals by precisely measuring voice-generated surface vibrations. Over long distances, however, the detected signal is very weak and full of speckle noise. To enhance the quality and intelligibility of the detected signal, we designed a two-sided Linear Prediction Coding (LPC)-based locator and interpolator to detect and replace speckle noise. We first studied the characteristics of speckle noise in detected signals and developed a binary-state statistical model for speckle noise generation. A two-sided LPC-based locator was then designed to locate the polluted samples, composed of an inverse decorrelator, nonlinear filter and threshold estimator. This greatly improves the detectability of speckle noise and avoids false/missed detection by improving the noise-to-signal-ratio (NSR). Finally, samples from both sides of the speckle noise were used to estimate the parameters of the interpolator and to code samples for replacing the polluted samples. Real-world speckle noise removal experiments and simulation-based comparative experiments were conducted and the results show that the proposed method is better able to locate speckle noise in laser detected speech and highly effective at replacing it.

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Enhancement of speech signals corrupted by impulsive noise using wavelets and adaptive median filtering

Cited by 2 publications

References 13 publications

A Novel Audio Signal De-noising Algorithm based on the Theory of Frequency Domain Analysis and Transform Domain Model

A Novel Audio Signal De-noising Algorithm based on the Theory of Frequency Domain Analysis and Transform Domain Model

Two-Sided LPC-Based Speckle Noise Removal for Laser Speech Detection Systems

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