论文标题
基于DNN的声学回声和删除噪声的任务分裂
Task splitting for DNN-based acoustic echo and noise removal
论文作者
论文摘要
神经网络为单任务语音增强带来了巨大的性能，例如抑制噪声和声音回声取消（AEC）。在这项工作中，我们评估使用单个关节或单独的模块来解决这些问题是否更有用。我们描述了不同的实现，并深入了解其性能和效率。我们表明，使用单独的回声取消模块和一个用于噪声和残留回声删除的模块会导致近端语音失真较小，并且在相同复杂性的双对词过程中表现更好。
Neural networks have led to tremendous performance gains for single-task speech enhancement, such as noise suppression and acoustic echo cancellation (AEC). In this work, we evaluate whether it is more useful to use a single joint or separate modules to tackle these problems. We describe different possible implementations and give insights into their performance and efficiency. We show that using a separate echo cancellation module and a module for noise and residual echo removal results in less near-end speech distortion and better performance during double-talk at same complexity.
