  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.
