  Being able to control the acoustic events (AEs) to which we want to listen
would allow the development of more controllable hearable devices. This paper
addresses the AE sound selection (or removal) problems, that we define as the
extraction (or suppression) of all the sounds that belong to one or multiple
desired AE classes. Although this problem could be addressed with a combination
of source separation followed by AE classification, this is a sub-optimal way
of solving the problem. Moreover, source separation usually requires knowing
the maximum number of sources, which may not be practical when dealing with
AEs. In this paper, we propose instead a universal sound selection neural
network that enables to directly select AE sounds from a mixture given
user-specified target AE classes. The proposed framework can be explicitly
optimized to simultaneously select sounds from multiple desired AE classes,
independently of the number of sources in the mixture. We experimentally show
that the proposed method achieves promising AE sound selection performance and
could be generalized to mixtures with a number of sources that are unseen
during training.
