|
|
---
|
|
|
license: mit
|
|
|
dataset_info:
|
|
|
features:
|
|
|
- name: audio
|
|
|
dtype: audio
|
|
|
- name: Speaker_Id
|
|
|
dtype: string
|
|
|
- name: SYSTEM_ID
|
|
|
dtype: string
|
|
|
- name: KEY
|
|
|
dtype: string
|
|
|
- name: Laundering_Type
|
|
|
dtype: string
|
|
|
- name: Laundering_Param
|
|
|
dtype: string
|
|
|
splits:
|
|
|
- name: train
|
|
|
num_bytes: 137488557853.713
|
|
|
num_examples: 2065873
|
|
|
download_size: 106529597527
|
|
|
dataset_size: 137488557853.713
|
|
|
configs:
|
|
|
- config_name: default
|
|
|
data_files:
|
|
|
- split: train
|
|
|
path: data/train-*
|
|
|
---
|
|
|
|
|
|
========================================================================================================
|
|
|
|
|
|
ASVspoof Laundered Database: This database is based on ASVspoof 2019 logical access (LA) eval partition.
|
|
|
|
|
|
The Asvspoof 2019 LA eval database is passed through five different types of additive noise at three
|
|
|
different Signal-to-Noise ratio (SNR) levels, three types of reverberation noise, six different re-compression rates, four
|
|
|
different resampling factors, and one type of low pass filtering accumulating to a total of 1388.22
|
|
|
hours of audio data.
|
|
|
|
|
|
Dataset Creators: Hashim Ali, Surya Subramani, Shefali Sudhir, Raksha Varahamurthy and Hafiz Malik
|
|
|
|
|
|
Dataset Contact: Hashim Ali alhashim@umich.edu
|
|
|
|
|
|
Date Written: 05/29/2024
|
|
|
|
|
|
*** WARNING ***
|
|
|
|
|
|
The 'flac' folder contains over 2 million (2065873) files. Open this folder at your own risk.
|
|
|
|
|
|
========================================================================================================
|
|
|
|
|
|
|
|
|
1. Directory Structure
|
|
|
|
|
|
--> ASVspoofLauneredDatabase
|
|
|
|
|
|
--> flac
|
|
|
|
|
|
--> protocols
|
|
|
|
|
|
--> Readme.txt
|
|
|
|
|
|
_________________________________
|
|
|
|
|
|
2. Description of the audio files
|
|
|
|
|
|
The directory flac contain audio files for each type of laundering attack, namely, Noise_Addition, Reverberation, Recompression, Resampling, and Filtering. Each laundering
|
|
|
attack (i) has different parameters (j) which are described below in the protocols section. All audio files in this directory are in the flac format.
|
|
|
|
|
|
_______________________________
|
|
|
|
|
|
3. Description of the protocols
|
|
|
|
|
|
The directory protocols contains five protocol files, one for each laundering attack.
|
|
|
|
|
|
Each column of the protocol is formatted as:
|
|
|
|
|
|
SPEAKER_ID AUDIO_FILE_NAME SYSTEM_ID KEY Laundering_Type Laundering_Param
|
|
|
|
|
|
1) SPEAKER_ID: LA_****, a 4-digit speaker ID
|
|
|
2) AUDIO_FILE_NAME: LA_****, name of the audio file
|
|
|
3) SYSTEM_ID: ID of the speech spoofing system (A01 - A19), or, for bonafide speech SYSTEM-ID is left blank ('-')
|
|
|
4) KEY: 'bonafide' for genuine speech, or, 'spoof' for spoofing speech
|
|
|
5) Laundering_Type Type of laundering attack. One of 'Noise_Addition', 'Reverberation', 'Recompression', 'Resampling', and 'Filtering'
|
|
|
6) Laundering_Param Parameters for the laundering attack. For example, in the case of Noise_Addition, it can be 'babble_0' where babble is the type of additive noise and 0 is the SNR level at which the babble noise is added to the audio signal.
|
|
|
|
|
|
**Note that**:
|
|
|
|
|
|
1) the first four columns are the same as in ASVspoof2019_LA_cm_protocols (refer to the ASVspoof2019 database), where the fourth in the original database
|
|
|
is omitted since it is NOT used for LA.
|
|
|
2) Brief description on the Laundering_Param:
|
|
|
|
|
|
babble_0 babble noise at SNR level of 0
|
|
|
babble_10 babble noise at SNR level of 10
|
|
|
babble_20 babble noise at SNR level of 20
|
|
|
cafe_0 cafe noise at SNR level of 0
|
|
|
cafe_10 cafe noise at SNR level of 10
|
|
|
cafe_20 cafe noise at SNR level of 20
|
|
|
street_0 street noise at SNR level of 0
|
|
|
street_10 street noise at SNR level of 10
|
|
|
street_20 street noise at SNR level of 20
|
|
|
volvo_0 volvo noise at SNR level of 0
|
|
|
volvo_10 volvo noise at SNR level of 10
|
|
|
volvo_20 volvo noise at SNR level of 20
|
|
|
white_0 white noise at SNR level of 0
|
|
|
white_10 white noise at SNR level of 10
|
|
|
white_20 white noise at SNR level of 20
|
|
|
RT_0_3 Reverberation with RT60 = 0.3 sec
|
|
|
RT_0_6 Reverberation with RT60 = 0.6 sec
|
|
|
RT_0_9 Reverberation with RT60 = 0.9 sec
|
|
|
recompression_128k Compression using bit rate of 128 kbit/s
|
|
|
recompression_16k Compression using bit rate of 16 kbit/s
|
|
|
recompression_196k Compression using bit rate of 196 kbit/s
|
|
|
recompression_256k Compression using bit rate of 256 kbit/s
|
|
|
recompression_320k Compression using bit rate of 320 kbit/s
|
|
|
recompression_64k Compression using bit rate of 64 kbit/s
|
|
|
resample_11025 resampling rate of 11025 Hz
|
|
|
resample_22050 resampling rate of 22050 Hz
|
|
|
resample_44100 resampling rate of 44100 Hz
|
|
|
resample_8000 resampling rate of 8000 Hz
|
|
|
lpf_7000 low pass filtering with a cut-off frequency of 7 Khz
|
|
|
|
|
|
**Citation**
|
|
|
```
|
|
|
@inproceedings{ali2024audio,
|
|
|
title={Is Audio Spoof Detection Robust to Laundering Attacks?},
|
|
|
author={Ali, Hashim and Subramani, Surya and Sudhir, Shefali and Varahamurthy, Raksha and Malik, Hafiz},
|
|
|
booktitle={Proceedings of the 2024 ACM Workshop on Information Hiding and Multimedia Security},
|
|
|
pages={283--288},
|
|
|
year={2024}
|
|
|
}
|
|
|
``` |
