Principle:Facebookresearch Audiocraft Audio Augmentation Effects
| Knowledge Sources | |
|---|---|
| Domains | Audio_Processing, Data_Augmentation |
| Last Updated | 2026-02-14 01:00 GMT |
Overview
A collection of differentiable and non-differentiable audio signal transformations used for data augmentation during model training to improve robustness.
Description
Audio Augmentation Effects encompass a suite of signal processing operations applied to training audio to improve model robustness. These include frequency-domain filters (lowpass, highpass, bandpass), temporal effects (speed change, echo, reverb), lossy compression (MP3), noise injection (white noise, pink noise), and dynamic range operations (compression, ducking, boosting). The effects are designed to simulate real-world audio degradations that a watermark or generation system must handle.
Usage
Use this principle when designing audio augmentation pipelines for training audio watermarking, compression, or generation models. Effects can be applied individually or selected probabilistically based on configurable weights.
Theoretical Basis
Each audio effect is a signal processing transformation T applied to the audio:
Pseudo-code:
# Abstract augmentation pipeline (NOT actual implementation)
effects = select_effects(available_effects, weights, mode="weighted")
for effect_name, effect_fn in effects:
augmented_audio = effect_fn(audio)
detection_result = model.detect(augmented_audio)
loss += detection_loss(detection_result, targets)