采用因果、低延迟、低复杂度方法的双耳多通道盲说话人分离
Binaural multichannel blind speaker separation with a causal low-latency and low-complexity approach
AI总结：
提出用于噪声混响环境的因果低延迟低复杂度双耳多通道盲说话人分离模型 GCBFSnet，通过 GC 显著降低模型规模与复杂度，并在多数指标上匹配 TasNet 基线。
摘要：
本文提出一种因果、低延迟、低复杂度方法，用于噪声混响条件下的双耳多通道盲说话人分离。该模型名为 Group Communication Binaural Filter and Sum Network（GCBFSnet，群通信双耳滤波求和网络），在时频域预测用于滤波求和波束形成的复数滤波器。我们采用 Group Communication（GC，群通信），即将潜在模型变量划分为多个组，并用一个共享序列模型处理，以降低仅含一个卷积模块和一个循环模块的简单模型的复杂度。借助 GC，与未使用 GC 的模型相比，我们能够将模型规模最多减小 83%，复杂度最多降低 73%，同时基本保持性能。即使对于最小的模型配置，GCBFSnet 在大多数指标上也能达到低复杂度 TasNet 基线的性能，尽管该基线具有更大的模型规模和更多的所需运算次数。
英文摘要：
In this paper, we introduce a causal low-latency low-complexity approach for binaural multichannel blind speaker separation in noisy reverberant conditions. The model, referred to as Group Communication Binaural Filter and Sum Network (GCBFSnet) predicts complex filters for filter-and-sum beamforming in the time-frequency domain. We apply Group Communication (GC), i.e., latent model variables are split into groups and processed with a shared sequence model with the aim of reducing the complexity of a simple model only containing one convolutional and one recurrent module. With GC we are able to reduce the size of the model by up to 83 % and the complexity up to 73 % compared to the model without GC, while mostly retaining performance. Even for the smallest model configuration, GCBFSnet matches the performance of a low-complexity TasNet baseline in most metrics despite the larger size and higher number of required operations of the baseline.
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。
