Abstract
Wavelet Neural Network (WNN) Is a Time-frequency Analysis Method, which Detects the Subtle Small Changes in the Signal Frequency Domain. Adaptive Filter Provides a Kind of Simple and Applied Method for Processing Signals in Noise. in this Paper, we Proposed a New Speech Enhancement Technique which Is Based on Wavelet Neural Network Using Adaptive Matched Filter Adjusting Weight. we Choose the Signal with Noise Pollution as the Input Signal and then Put it to the Trained Wavelet Neural Network. Wavelet Decomposition and Wavelet Neural Network Weights Processing Adopt Signal Sub-band Adaptive Matched Filter, the Output Signal of Wavelet Neural Network Is an Approximation Form of Original Signal. the Results Show that the WNN Is a Quite Effective Method for the Speech Enhancement and Improving the Ration of Signal to Noise.
Highlights
The speech signal is often accompanied by the environment noise [1]
Speech enhancement plays an important role in the recognition or compression of speech signals system performances
This paper presents a comprehensive investigation of practicality of using Wavelet neural network (WNN) and sub-band adaptive matched filter to extract clean speech signals from the noisy environment
Summary
The speech signal is often accompanied by the environment noise [1]. speech enhancement plays an important role in the recognition or compression of speech signals system performances. This paper presents a comprehensive investigation of practicality of using WNN and sub-band adaptive matched filter to extract clean speech signals from the noisy environment. The used approach is to achieve the clean speech with wavelet neural network and sub-band adaptive matched filter.
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