FLAD
Fake Lossless Audio Detector
Feature
- Determine the audio source format:
Lossless, AAC, MP3(ex. SoundCloud), Opus(ex. YouTube) - Not rely on high frequency information:
Frequencies below 2.4kHz or above 20kHz are not used - Resists minor noise interference:
Noisy samples included in training data
Demo
Audio: AAC downloaded from YouTube
Post-processing: EmiyaEngine
Spectrum:
Lossless Audio Checker (not work):
Ours:
Performance
Test set: 200 samples per category (50% with noise)
Result: 798 correct identifications out of 800 samples with 99.75 % accuracy
Dependence
eval only:
- librosa >= 0.8.0
- resampy
- matplotlib
- numpy
- Pillow
- onnxruntime >= 1.5.2
train & test:
- pytorch >= 1.5.1
- efficientnet_pytorch
- livelossplot (option)
export model:
- onnx-simplifier
Usage
generate dataset:
# set input & output path
audio_root = './music'
ds_root = './dataset'
then python generate.py
train model:
python train.py
test model (in pytorch):
python test.py
export model to onnx:
python export.py
eval model (in onnxruntime):
run python eval.py fake.flac