mirror of
https://github.com/macaodha/batdetect2.git
synced 2026-04-04 15:20:19 +02:00
37 lines
1010 B
Markdown
37 lines
1010 B
Markdown
# 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/configure-audio-preprocessing`
|
|
- Configure spectrogram preprocessing:
|
|
{doc}`../how_to/configure-spectrogram-preprocessing`
|
|
- Preprocessing config reference: {doc}`../reference/preprocessing-config`
|