Comments (1)
Hi there,
I don't have a strong reason to choose one over another. But I felt that fbank is more standard for audio application as it provides some flexibility on the choice of filters (in this repo, mel filters). Having said that, I haven't formally studied if mel filters are better for general audio classification. I would expect the normalization stats for spectrogram would be different with fbanks, so if you want to choose to use spectrogram, please double check the normalization mean and std (especially for ImageNet pretrained models).
-Yuan
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Related Issues (12)
- class_labels_indices.csv is missing in psla/egs/fsd50k/class_labels_indices.csv HOT 1
- file `ontology.json` not found HOT 3
- regarding dataset prep scripts and audioset splits HOT 4
- inference script HOT 2
- Number of parameters of the model HOT 2
- Pretrained models HOT 1
- Output of MHA EfficientNet model HOT 4
- using gen_weight_File HOT 1
- impact of enhanced labeling on fsd50k
- Pretrained enhanced label set HOT 1
- prep_fsd.py problems HOT 4
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