论文标题
大小还是相？两级算法的替代算法
Magnitude or Phase? A Two Stage Algorithm for Dereverberation
论文作者
论文摘要
在这项工作中，我们提出了一种新的单微粒语音语音替代算法。首先，提出了绩效分析，以解释算法专注于改善幅度或相位的算法不够好。此外，我们证明，几乎没有客观测量与干净的幅度具有很高的相关性，而其他客观测量与干净相位。因此，我们提出了一个由两个子模型组成的新体系结构，每个子模型都负责另一个任务。在嘈杂的输入下，第一个模型估计了干净的幅度。然后将增强的幅度与嘈杂的输入阶段一起用作第二个模型的输入，以估计缩放信号的实际和虚构部分。论文中提出了包括预培训和微调的培训计划。我们使用Reverb挑战中的数据评估我们提出的方法，并将我们的结果与其他方法进行比较。我们证明了所有度量的一致改进，这可以归因于大小和相位的改进估计值。
In this work we present a new single-microphone speech dereverberation algorithm. First, a performance analysis is presented to interpret that algorithms focused on improving solely magnitude or phase are not good enough. Furthermore, we demonstrate that few objective measurements have high correlation with the clean magnitude while others with the clean phase. Consequently ,we propose a new architecture which consists of two sub-models, each of which is responsible for a different task. The first model estimates the clean magnitude given the noisy input. The enhanced magnitude together with the noisy-input phase are then used as inputs to the second model to estimate the real and imaginary portions of the dereverberated signal. A training scheme including pre-training and fine-tuning is presented in the paper. We evaluate our proposed approach using data from the REVERB challenge and compare our results to other methods. We demonstrate consistent improvements in all measures, which can be attributed to the improved estimates of both the magnitude and the phase.
