  Self-supervised learning (SSL) has proven vital in speech and audio-related
applications. The paradigm trains a general model on unlabeled data that can
later be used to solve specific downstream tasks. This type of model is costly
to train as it requires manipulating long input sequences that can only be
handled by powerful centralised servers. Surprisingly, despite many attempts to
increase training efficiency through model compression, the effects of
truncating input sequence lengths to reduce computation have not been studied.
In this paper, we provide the first empirical study of SSL pre-training for
different specified sequence lengths and link this to various downstream tasks.
We find that training on short sequences can dramatically reduce resource costs
while retaining a satisfactory performance for all tasks. This simple one-line
change would promote the migration of SSL training from data centres to
user-end edge devices for more realistic and personalised applications.

Sound;Machine Learning;Audio and Speech Processing