Masked Contrastive Pre-Training Improves Music Audio Key Detection
Self-supervised music foundation models underperform on key detection, which requires pitch-sensitive representations. In this work, we present the first systematic study showing that the design of self-supervised pretraining directly impacts pitch sensitivity, and demonstrate that masked contrastive embeddings uniquely enable state-of-the-art (SOTA) performance in key detection in the supervised setting. First, we discover that linear evaluation after masking-based contrastive pretraining on Mel spectrograms leads to competitive performance on music key detection out of the box. This leads us to train shallow but wide multi-layer perceptrons (MLPs) on features extracted from our base mod...
| Authors | Ori Yonay, Tracy Hammond, Tianbao Yang |
|---|---|
| Primary category | cs.SD |
| Published | 11 April 2026 |
| Last updated at source | 11 April 2026 |
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