神经网络语音分离模型训练中的数据采样策略（英文版）

英文标题：On Data Sampling Strategies for Training Neural Network Speech Separation Models中文摘要：本文研究了在语音分离模型中应用不同的训练信号长度的影响，发现特定分布情况下应用特定的训练信号长度会提高模型性能，同时使用动态混合和最佳信号长度训练的模型被证明具有最佳性能。英文摘要：Speech separation remains an important area of multi-speaker signalprocessing. Deep neural network (DNN) models have attained the best performanceon many speech separation benchmarks. Some of these models can take significanttime to train and have high memory requirements. Previous work has proposedshortening trainin