DRNN在激光多普勒测振仪测声系统中的应用

Application of DRNN in voice measurement system of laser Doppler vibrometer

为了降低激光多普勒测振仪在测声过程中给语音信号中引入的噪声,采用深度循环神经网络语音信号去噪的方法,对从激光多普勒测声系统采集回来的语音信号做降噪处理,并进行了理论分析和实验验证。结果表明,利用层数为1层～3层、每层神经元个数为1024的深度循环神经网络,对-6dB～6dB信噪比的语音信号进行处理,随着层数的增加,语音信号的质量在多项评价指标上达到8dB～12dB的提升;深度循环神经网络可以有效对激光多普勒测声系统采集的语音信号进行降噪处理。该研究对提升语音信号的质量有着实际意义。

In order to reduce the noise introduced to speech signal by a laser Doppler vibrometer during the measurement of sound,the method of deep recurrent neural network(DRNN)speech signal denoising was adopted.The speech signal collected from laser Doppler measurement system was denoised.By using the deep recurrent neural network with1layer-3layers and1024neurons per layer,the speech signals with signal-to-noise ratio from-6dB to6dB were processed.After theoretical analysis and experimental verification,the results show that,as the number of layers increases,the quality of speech signals has risen to8dB-12dB in many evaluation indexes.DRNN can effectively denoise the speech signals collected by laser Doppler acoustic system.The research is of practical significance for improving the quality of speech signals.