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Preprocessing consistency
Preprocessing consistency is one of the biggest factors behind stable model performance.
Why consistency matters
The detector is trained on spectrograms produced by a specific preprocessing pipeline. If inference uses different settings, the model can see a shifted input distribution and performance may drop.
Typical mismatch sources:
- sample-rate differences,
- changed frequency crop,
- changed STFT window/hop,
- changed spectrogram transforms.
Practical implication
When possible, keep preprocessing settings aligned between:
- training,
- evaluation,
- deployment inference.
If you intentionally change preprocessing, treat this as a new experiment and re-validate on reviewed local data.
Related pages
- Configure audio preprocessing:
{doc}
../how_to/data/configure-audio-preprocessing - Configure spectrogram preprocessing:
{doc}
../how_to/data/configure-spectrogram-preprocessing - Preprocessing config reference:
{doc}
../reference/configs/data/preprocessing-config