## 2 Materials and Methods
### An Ensemble of Methods for Audio Tampering Detection
#### 2.3.3 Reverberation Level Estimation  
Another indicator that several segments of a FUI may have been inserted from a different file is the effect of acoustic conditions on the recording. Every space has different reverberation levels that affect the recording. Especially since most newsworthy events are not recorded in ideal conditions of professional recording studios, a regression model based on a Convolutional Neural Network architecture [47] was trained using a big dataset of simulated reverberation to provide a numerical estimations of the Signal-to-Reverberation ratio for every audio segment. Segments with outlier values are possibly related to malicious audio splicing.  
Convolutional Neural Networks (CNNs) are a type of deep learning architecture that have gained popularity in audio recognition and event detection tasks [55; 56]. One main reason for their recent widespread is is that there is no need for a handcrafted feature vector. Instead, a visual representation of the audio information is fed to the networks as an image, and the input layers extract hierarchical features in an unsupervised manner during training. Different kinds of input have been evaluated for deep learning techniques, with spectrograms being the dominant approach [54].  
Signal-to-Reverberation-Ratio (SRR) can be a useful attribute that can indicate audio slicing. SRR expresses the ratio of the energy of the direct acoustic field to the reverberation acoustic field. It is determined by the acoustic characteristics of the space of the recording, the positioning of the sound source and the recording device. The distance where the levels of the direct and the reverberation sound are equal (SRR = 1) is called the critical distance. At distances closer than the critical distance, we can assume SRR > 1, and at distances that are farther than the critical distance, SRR < 1. The critical distance itself depends on the room acoustic attributes.  
For recordings that take place under different conditions, the SRR is expected to differ. When segments from different recordings are pieced together, it is possible to detect the inconsistency in their SRR, even if it is not audible by human listeners. Calculating the SRR for different time windows can provide another criterion for audio tampering detection.  
In the proposed approach, a deep learning regression model is used for a data-driven estimation of the SRR, based on simulation data. A 3600-second-long audio file containing pink noise was created, using the Adobe Audition generator. Using the same software, reverberation was added to the file with different SRRs. Ten different SRRs were chosen, resulting in 11 audio files (including the original), producing a 39600-second-long dataset. The same source audio file was used for all SRRs, so that the model is trained to recognize the reverberation and not information related to the content of different audio streams. The selected SRRs are shown in Table 1.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} Signal (\%) & 100 & 90 & 80 & 70 & 60 & 50 & 40 & 30 & 20 & 10 & 0 \\ Reverberation (\%) & 0 & 10 & 20 & 30 & 40 & 50 & 60 & 70 & 80 & 90 & 100 \\ \end{tabular}
\end{table}
Table 1: The different Signal-to-Reverberation Ratios that were used for the model training.  
The dataset was used for the training of a CNN regression model. The output of the model is a continuous value from 0 (no reverberation) to 1 (only reverberation). The model architecture is provided in Table 2.