Rapid and brief communication: Wavelet feature domain adaptive noise reduction using learning algorithm for text-independent speaker recognition

Authors:
Shung-Yung Lung
Affiliations:
Department of Information and Telecommunications Engineering, Ming Chuan University, Taoyuan County, Taiwan
Venue:
Pattern Recognition
Year:
2007

Citing 8
Cited 2

Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
Wavelet feature selection based neural networks with application to the text independent speaker identification

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Feature extracted from wavelet decomposition using biorthogonal Riesz basis for text-independent speaker recognition

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Spectrum estimation by wavelet thresholding of multitaperestimators

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Wavelet transform domain adaptive FIR filtering

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Wavelet transform based adaptive filters: analysis and new results

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De-noising by soft-thresholding

IEEE Transactions on Information Theory
Magnified gradient function with deterministic weight modification in adaptive learning

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Wavelet-based multiresolution analysis for data cleaning and its application to water quality management systems

Expert Systems with Applications: An International Journal
Feature extracted from wavelet decomposition using biorthogonal Riesz basis for text-independent speaker recognition

Pattern Recognition

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Abstract

In this paper, a type of thresholding method is developed for adaptive noise reduction. Here, we propose a new type thresholding method. Unlike the standard thresholding functions, the new thresholding functions are infinitely differentiable. Gradient-based adaptive learning algorithms are presented to seek the optimal solution for noise reduction. Furthermore, the learning algorithm can be used for any speaker data derived from discrete wavelet transform. It is demonstrated that 94% correct classification rates can be achieved by the use of the first 32 variation features in TALUNG database.