  We introduce the Free Universal Sound Separation (FUSS) dataset, a new corpus
for experiments in separating mixtures of an unknown number of sounds from an
open domain of sound types. The dataset consists of 23 hours of single-source
audio data drawn from 357 classes, which are used to create mixtures of one to
four sources. To simulate reverberation, an acoustic room simulator is used to
generate impulse responses of box shaped rooms with frequency-dependent
reflective walls. Additional open-source data augmentation tools are also
provided to produce new mixtures with different combinations of sources and
room simulations. Finally, we introduce an open-source baseline separation
model, based on an improved time-domain convolutional network (TDCN++), that
can separate a variable number of sources in a mixture. This model achieves 9.8
dB of scale-invariant signal-to-noise ratio improvement (SI-SNRi) on mixtures
with two to four sources, while reconstructing single-source inputs with 35.5
dB absolute SI-SNR. We hope this dataset will lower the barrier to new research
and allow for fast iteration and application of novel techniques from other
machine learning domains to the sound separation challenge.
