Masked Spectrogram Modeling using Masked Autoencoders for Learning General-purpose Audio Representation

Understand

Recent general-purpose audio representations show state-of-the-art performance on various audio tasks.

• These representations are pre-trained by self-supervised learning methods that create training signals from the input.
• For example, typical audio contrastive learning uses temporal relationships among input sounds to create training signals, whereas some methods use a difference among input views created by data augmentations.
• However, these training signals do not provide information derived from the intact input sound, which we think is suboptimal for learning representation that describes the input as it is.