  Despite progress in audio classification, a generalization gap remains
between speech and other sound domains, such as environmental sounds and music.
Models trained for speech tasks often fail to perform well on environmental or
musical audio tasks, and vice versa. While self-supervised (SSL) audio
representations offer an alternative, there has been limited exploration of
scaling both model and dataset sizes for SSL-based general audio
classification. We introduce Dasheng, a simple SSL audio encoder, based on the
efficient masked autoencoder framework. Trained with 1.2 billion parameters on
272,356 hours of diverse audio, Dasheng obtains significant performance gains
on the HEAR benchmark. It outperforms previous works on CREMA-D, LibriCount,
Speech Commands, VoxLingua, and competes well in music and environment
classification. Dasheng features inherently contain rich speech, music, and
environmental information, as shown in nearest-neighbor classification
experiments. Code is available https://github.com/richermans/dasheng/.
