期刊论文

Speech enhancement algorithm based on time-frequency joint loss function

中文

计算机应用

1001-9081

2022

42

S01

316-320

10.11772/j.issn.1001-9081.2021071325

本校为其他机构

在频域语音增强算法中，由于估计幅度谱与带噪相位谱的不匹配，其性能难以突破固有上限。在时域语音增强框架中，模型将时域波形作为输入，由网络直接学习时域波形之间的映射关系，有效地避免了无效短时傅里叶变换（STFT）问题。然而，常见的采用波形最小均方误差的时域语音增强算法对语音频域特征的建模并未达到最优。针对这个问题，提出一种基于时频联合损失函数的语音增强算法。首先将时频联合损失函数应用到Wave-U-Net时域语音增强网络，同时设计并分析了一阶范数形式和二阶范数形式的时频联合损失函数对增强网络的影响，最后得到了面向语音通信任务和语音识别任务的相对最佳损失函数选择方...

In the frequency-domain speech enhancement algorithms,the performance is difficult to break the inherent upper limit due to the mismatch between the estimated amplitude spectrum and the band-noise phase spectrum.In the time-domain speech enhancement framework,time-domain waveform is taken as the input of the model and the mapping relationship between time-domain waveforms is learned directly by the network,which effectively avoids the invalid Short-Time Fourier Transform(STFT)problem.However,the common time-domain speech enhancement algorithm using waveform minimum mean square error does not a...