混响环境中双流结构时域深度吸引子网络的研究
Exploring the time-domain deep attractor network with two-stream architectures in a reverberant environment
摘要：
深度吸引子网络（DAN）通过区分性嵌入和说话人吸引子来执行语音分离。与基于排列不变训练（PIT）的方法相比，DAN定义了一个深度嵌入空间，并在每个时频（T-F）bin上提供更精细的表示。然而，已经观察到，如果直接部署在混响环境中，则DAN在信号质量上实现有限的改善。继时域分离网络在纯净混合语音上取得成功之后，我们提出了一种具有两个卷积网络流的时域DAN（TD-DAN），该网络在可变数量扬声器的条件下有效地执行去混响和分离任务。TD-DAN的说话人编码流（SES）被训练以在嵌入空间中对说话人信息进行建模。语音解码流（SDS）接受来自SES的扬声器吸引子，并学习从频谱-时间表示中估计早期反射。同时，额外的聚类损失被用来弥合预言和估计的吸引子之间的差距。实验在空间化多发言人华尔街日报（SMS-WSJ）数据集上进行。将早期反射信号与消声信号和混响信号进行比较，选择早期反射信号作为学习目标。实验结果表明，TD-DAN在混响2/3扬声器评估集上实现了9.79/7.47 dB的尺度不变源失真比（SI-SDR）增益，分别超过基线DAN和卷积时域音频分离网络（Conv-TasNet）1.92/0.68 dB和0.91/0.47 dB。
英文摘要：
Deep attractor networks (DANs) perform speech separation with discriminative embeddings and speaker attractors. Compared with methods based on the permutation invariant training (PIT), DANs define a deep embedding space and deliver a more elaborate representation on each time-frequency (T-F) bin. However, it has been observed that the DANs achieve limited improvement on the signal quality if directly deployed in a reverberant environment. Following the success of time-domain separation networks on the clean mixture speech, we propose a time-domain DAN (TD-DAN) with two-streams of convolutional networks, which efficiently perform both dereverberation and separation tasks under the condition of a variable number of speakers. The speaker encoding stream (SES) of the TD-DAN is trained to model the speaker information in the embedding space. The speech decoding stream (SDS) accepts speaker attractors from the SES and learns to estimate early reflections from the spectro-temporal representations. Meanwhile, additional clustering losses are used to bridge the gap between the oracle and the estimated attractors. Experiments were conducted on the Spatialized Multi-Speaker Wall Street Journal (SMS-WSJ) dataset. The early reflection was compared with the anechoic and reverberant signals and then was chosen as the learning targets. The experimental results demonstrated that the TD-DAN achieved scale-invariant source-to-distortion ratio (SI-SDR) gains of 9.79/7.47 dB on the reverberant 2/3-speaker evaluation set, exceeding the baseline DAN and convolutional time-domain audio separation network (Conv-TasNet) by 1.92/0.68 dB and 0.91/0.47 dB, respectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
