Speech separation remains an important area of multi-speaker signal
processing. Deep neural network (DNN) models have attained the best performance
on many speech separation benchmarks. Some of these models can take significant
time to train and have high memory requirements. Previous work has proposed
shortening training examples to address these issues but the impact of this on
model performance is not yet well understood. In this work, the impact of
applying these training signal length (TSL) limits is analysed for two speech
separation models: SepFormer, a transformer model, and Conv-TasNet, a
convolutional model. The WJS0-2Mix, WHAMR and Libri2Mix datasets are analysed
in terms of signal length distribution and its impact on training efficiency.
It is demonstrated that, for specific distributions, applying specific TSL
limits results in better performance. This is shown to be mainly due to
randomly sampling the start index of the waveforms resulting in more unique
examples for training. A SepFormer model trained using a TSL limit of 4.42s and
dynamic mixing (DM) is shown to match the best-performing SepFormer model
trained with DM and unlimited signal lengths. Furthermore, the 4.42s TSL limit
results in a 44% reduction in training time with WHAMR.

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。

神经网络语音分离模型训练中的数据采样策略

On Data Sampling Strategies for Training Neural Network Speech  Separation Models

Recurrent Neural Networks (RNNs) have long been the dominant architecture in
sequence-to-sequence learning. RNNs, however, are inherently sequential models
that do not allow parallelization of their computations. Transformers are
emerging as a natural alternative to standard RNNs, replacing recurrent
computations with a multi-head attention mechanism. In this paper, we propose
the SepFormer, a novel RNN-free Transformer-based neural network for speech
separation. The SepFormer learns short and long-term dependencies with a
multi-scale approach that employs transformers. The proposed model achieves
state-of-the-art (SOTA) performance on the standard WSJ0-2/3mix datasets. It
reaches an SI-SNRi of 22.3 dB on WSJ0-2mix and an SI-SNRi of 19.5 dB on
WSJ0-3mix. The SepFormer inherits the parallelization advantages of
Transformers and achieves a competitive performance even when downsampling the
encoded representation by a factor of 8. It is thus significantly faster and it
is less memory-demanding than the latest speech separation systems with
comparable performance.

本文介绍了一种基于 Transformers、无 RNN 结构的深度神经网络，即 SepFormer，并运用多尺度方法使其实现短时和长时依赖性的学习，从而在语音分离任务中取得了最优结果，并具有较高的计算速度和较小的内存占用。