Speech Enhancement with Fractional Fourier Transform

Cun Zhu, Yan Sun, Chun-zhi Pan
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Abstract

The Fast Discrete Fourier Transform is commonly analysis method for speech signal processing in frequency domain. It has the problem of adjusting window size for desired resolution. But the Fractional Fourier Transform can have both time domain and frequency domain processing capabilities. This paper performs global processing by combining Fractional Fourier Transform and wavelet multi-scale local elimination on the speech signal and enhancement experiment with RNN neural network. The experimental results show that the speech signal is processed both locally by wavelet and globally by Fractional Fourier Transform. The combination of the two allows the speech signal to take into account both local and global information in the time and frequency domain.
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分数阶傅里叶变换的语音增强
快速离散傅里叶变换是频域语音信号处理的常用分析方法。它有调整窗口大小以满足所需分辨率的问题。但是分数阶傅里叶变换可以同时具有时域和频域处理能力。本文结合分数阶傅立叶变换和小波多尺度局部消去对语音信号进行全局处理,并用RNN神经网络进行增强实验。实验结果表明,小波变换对语音信号进行局部处理,分数阶傅里叶变换对语音信号进行全局处理。这两者的结合使得语音信号在时域和频域同时考虑到局部和全局信息。
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