https://github.com/nesl/asvspoof2019
Our submission to the ASVspoof 2019: Automatic Speaker Verification Spoofing and Countermeasures Challenge
https://github.com/nesl/asvspoof2019
Last synced: 5 months ago
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Our submission to the ASVspoof 2019: Automatic Speaker Verification Spoofing and Countermeasures Challenge
- Host: GitHub
- URL: https://github.com/nesl/asvspoof2019
- Owner: nesl
- Created: 2019-02-01T21:01:15.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2020-02-20T04:35:08.000Z (over 6 years ago)
- Last Synced: 2023-03-10T21:57:01.597Z (over 3 years ago)
- Language: MATLAB
- Size: 136 MB
- Stars: 81
- Watchers: 12
- Forks: 39
- Open Issues: 20
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Deep Residual Neural Networks for Audio Spoofing Detection
This repo has an implementation for our paper **Deep Residual Neural Networks for Audio Spoofing Detection**, this is also describes the solution of team **UCLANESL** in the [ASVSpoof 2019 competition](https://www.asvspoof.org/).
## Dataset
The ASVSpoof2019 dataset can be downloaded from the following link:
[ASVSpoof2019 dataset](https://datashare.is.ed.ac.uk/handle/10283/3336)
### Training models
```
python model_main.py --num_epochs=100 --track=[logical/physical] --features=[spect/mfcc/cqcc] --lr=0.00005
```
Please note that the CQCC features are computing using the Matlab code in [cqcc_extraction.m](./cqcc_extraction.m), so you need to run this file to generate cache files of CQCC featurs before attempting to traiin or evaluate models with CQCC features.
#### To perform fusion of multiple results files
```
python fuse_result.py --input FILE1 FILE2 FILE3 --output=RESULTS_FILE
```
### Evaluating Models
Run the model on the evaluation dataset to generate a prediction file.
```
python model_main.py --eval --eval_output=RESULTS_FILE --model_path=CHECKPOINT_FILE
```
Then compute the evaluation scores using on the development dataset
```
python evaluate_tDCF_asvspoof19.py RESULTS_FILE PATH_TO__asv_dev.txt
```