基于深度学习的语音信号去噪方法研究

Research on Speech Signal Denoising Methods Based on Deep Learning

为提升语音去噪技术的整体性能,提出基于深度学习的语音信号去噪方法。通过引入Adam优化长短期记忆(Long Short-Term Memory,LSTM)网络,提高了语音去噪的效率和效果。实验部分使用TIMIT数据集进行验证,并对比了传统LSTM模型和优化后的LSTM模型的性能。实验结果表明,优化后的LSTM模型的信噪比(Signal-to-Noise Ratio,SNR)和均方误差(Mean Squared Error,MSE)均优于传统模型。

To improve the overall performance of speech denoising technology,a deep learning based speech signal denoising method is proposed.By introducing Adam to optimize Long Short-Term Memory(LSTM)networks,the efficiency and effectiveness of speech denoising have been improved.The experimental part was validated using the TIMIT dataset,and the performance of the traditional LSTM model and the optimized LSTM model were compared.The experimental results show that the optimized LSTM model has better Signal-to-Noise Ratio(SNR)and Mean Squared Error(MSE)than traditional models.