Paper ID: 2409.09546

Effective Pre-Training of Audio Transformers for Sound Event Detection

Florian Schmid, Tobias Morocutti, Francesco Foscarin, Jan Schlüter, Paul Primus, Gerhard Widmer

We propose a pre-training pipeline for audio spectrogram transformers for frame-level sound event detection tasks. On top of common pre-training steps, we add a meticulously designed training routine on AudioSet frame-level annotations. This includes a balanced sampler, aggressive data augmentation, and ensemble knowledge distillation. For five transformers, we obtain a substantial performance improvement over previously available checkpoints both on AudioSet frame-level predictions and on frame-level sound event detection downstream tasks, confirming our pipeline's effectiveness. We publish the resulting checkpoints that researchers can directly fine-tune to build high-performance models for sound event detection tasks.

Submitted: Sep 14, 2024