标题： HD-DEMUCS：具有异构解码器的通用语音恢复 显示英文标题

标题： HD-DEMUCS: General Speech Restoration with Heterogeneous Decoders

摘要： 本文介绍了一种端到端的神经语音恢复模型HD-DEMUCS，在多种失真环境中表现出有效性。与传统方法不同，传统方法采用级联框架首先去除不需要的噪声，然后恢复缺失的信号成分，我们的模型使用两个异构解码器网络并行执行这些任务。基于U-Net风格的编码器-解码器框架，我们附加了一个额外的解码器，使得每个解码器网络分别执行噪声抑制或恢复。我们精心设计每个解码器架构，根据其目标适当运行。此外，我们通过利用可学习的权重因子，聚合两个解码器输出波形来提高性能。在各种环境下的客观度量实验结果清楚地表明，我们的方法在一般的语音恢复任务中优于单个解码器或多阶段系统。 显示英文摘要

摘要： This paper introduces an end-to-end neural speech restoration model, HD-DEMUCS, demonstrating efficacy across multiple distortion environments. Unlike conventional approaches that employ cascading frameworks to remove undesirable noise first and then restore missing signal components, our model performs these tasks in parallel using two heterogeneous decoder networks. Based on the U-Net style encoder-decoder framework, we attach an additional decoder so that each decoder network performs noise suppression or restoration separately. We carefully design each decoder architecture to operate appropriately depending on its objectives. Additionally, we improve performance by leveraging a learnable weighting factor, aggregating the two decoder output waveforms. Experimental results with objective metrics across various environments clearly demonstrate the effectiveness of our approach over a single decoder or multi-stage systems for general speech restoration task.