  The performance of neural network-based speech enhancement systems is
primarily influenced by the model architecture, whereas training times and
computational resource utilization are primarily affected by training
parameters such as the batch size. Since noisy and reverberant speech mixtures
can have different duration, a batching strategy is required to handle variable
size inputs during training, in particular for state-of-the-art end-to-end
systems. Such strategies usually strive for a compromise between zero-padding
and data randomization, and can be combined with a dynamic batch size for a
more consistent amount of data in each batch. However, the effect of these
strategies on resource utilization and more importantly network performance is
not well documented. This paper systematically investigates the effect of
different batching strategies and batch sizes on the training statistics and
speech enhancement performance of a Conv-TasNet, evaluated in both matched and
mismatched conditions. We find that using a small batch size during training
improves performance in both conditions for all batching strategies. Moreover,
using sorted or bucket batching with a dynamic batch size allows for reduced
training time and GPU memory usage while achieving similar performance compared
to random batching with a fixed batch size.
