In this study, a novel multi-objective speech enhancement algorithm is proposed. First, we construct a deep learning architecture based on a stacked and temporal convolutional neural network (STCNN). Second, the main log-power spectra (LPS) features are input into a stacked convolutional neural network (SCNN) to extract advanced abstract features. Third, an improved power function compression Mel-frequency cepstral coefficient (PC-MFCC) feature—more consistent with human hearing characteristics than a Mel-frequency cepstral coefficient (MFCC)—is proposed. Then, a temporal convolutional neural network (TCNN) uses PC-MFCC and learned features from SCNN as input, and separately predicts a clean LPS, PC-MFCC and Ideal Ratio Mask (IRM). In this training phase, PC-MFCC constrains the LPS and IRM through a loss function to obtain the optimal network structure. Finally, IRM-based post-processing is used on the estimated clean LPS and IRM, which adjusts the weight between the above LPS and IRM to synthesise enhanced speech based on voice presence information. A series of experiments show that PC-MFCC is effective and shows complementarity with LPS in speech enhancement tasks. The proposed STCNN architecture has a higher speech enhancement performance than the comparative neural network models with good feature extraction and sequence modelling capabilities. Additionally, IRM-based post-processing further enhances the listening quality of reconstructed speech. Compared with the contrasting algorithm, the speech quality and intelligibility of enhanced speech based on the proposed multi-objective speech enhancement algorithm are further improved.

在这项研究中，提出了一种新颖的多目标语音增强算法。首先，我们基于堆叠的时间卷积神经网络（STCNN）构建深度学习架构。其次，将主要的对数功率谱（LPS）特征输入到堆叠的卷积神经网络（SCNN）中，以提取高级抽象特征。第三，提出了一种改进的幂函数压缩梅尔频率倒谱系数（PC-MFCC）特征（比梅尔频率倒谱系数（MFCC）更符合人类的听觉特性）。然后，时间卷积神经网络（TCNN）使用PC-MFCC和从SCNN学习的特征作为输入，并分别预测干净的LPS，PC-MFCC和理想比率掩码（IRM）。在这个训练阶段 PC-MFCC通过损耗函数约束LPS和IRM，以获得最佳的网络结构。最后，在估计的干净LPS和IRM上使用基于IRM的后处理，它可以调整上述LPS和IRM之间的权重以基于语音存在信息合成增强语音。一系列实验表明，PC-MFCC是有效的，并且在语音增强任务中与LPS具有互补性。与具有良好特征提取和序列建模能力的比较神经网络模型相比，所提出的STCNN体系结构具有更高的语音增强性能。此外，基于IRM的后处理进一步提高了重建语音的收听质量。与对比算法相比，