  General-purpose embedding is highly desirable for few-shot even zero-shot
learning in many application scenarios, including audio tasks. In order to
understand representations better, we conducted a thorough error analysis and
visualization of HEAR 2021 submission results. Inspired by the analysis, this
work experiments with different front-end audio preprocessing methods,
including Constant-Q Transform (CQT) and Short-time Fourier transform (STFT),
and proposes a Batch Embedding Covariance Regularization (BECR) term to uncover
a more holistic simulation of the frequency information received by the human
auditory system. We tested the models on the suite of HEAR 2021 tasks, which
encompass a broad category of tasks. Preliminary results show (1) the proposed
BECR can incur a more dispersed embedding on the test set, (2) BECR improves
the PaSST model without extra computation complexity, and (3) STFT
preprocessing outperforms CQT in all tasks we tested.
Github:https://github.com/ankitshah009/general_audio_embedding_hear_2021
