  The goal of universal audio representation learning is to obtain foundational
models that can be used for a variety of downstream tasks involving speech,
music and environmental sounds. To approach this problem, methods inspired by
works on self-supervised learning for NLP, like BERT, or computer vision, like
masked autoencoders (MAE), are often adapted to the audio domain. In this work,
we propose masking representations of the audio signal, and training a MAE to
reconstruct the masked segments. The reconstruction is done by predicting the
discrete units generated by EnCodec, a neural audio codec, from the unmasked
inputs. We evaluate this approach, which we call EnCodecMAE, on a wide range of
tasks involving speech, music and environmental sounds. Our best model
outperforms various state-of-the-art audio representation models in terms of
global performance. Additionally, we evaluate the resulting representations in
the challenging task of automatic speech recognition (ASR), obtaining decent
results and paving the way for a universal audio representation.
