# Audio Steganalysis Estimation with the Goertzel Algorithm
## 4 Statistical Coefficient Results
### Audio Quality Metrics Comparative Results
#### 4.2.3 Perceptual Estimates  
In this area, audio vectors oriented toward the perception of the human auditory system are analyzed.  
Bark Spectral Distortion (BSD): BSD is based on the assumption that speech quality is directly related to speech loudness, which is a psychoacoustic term defined as the magnitude of auditory noise [8; 30; 36; 37].  
Modified Bark Spectral Distortion (MBSD): MBSD is a modification of BSD that incorporates noise threshold masking to differentiate between audible and inaudible distortions [8; 30; 36].  
Enhanced Modified Bark Spectral Distortion (EMBSD): EMBSD is a variation of MBSD where only the first 15 loudness components are worked on, rather than the 24 Bark bands that are used to calculate loudness differences [8].  
Perceptual Audio Quality Measure (PAQM): The PAQM emulates the human auditory system and is used for the transformation from the physical to the psychophysical domain [8; 30; 36].  
Perceptual Speech Quality Measure (PSQM): PSQM is an optimized PAQM algorithm for speech [8; 30].  
Weighted Slope Spectral Distance Measure (WSSD): WSSD obtains the weighted difference between the spectral slopes in each band of the filter bank. The magnitude of each weight reflects whether the band is close to a spectral peak or valley and whether the peak is the largest in the spectrum [8; 30; 31; 32; 36].  
In the following tables, we show the results obtained from the tests carried out on the audio files, making comparisons between the originals and the possible stegoaudios. We show the results of the audio quality metrics by applying them to the audio files without the Goertzel algorithm and with the Goertzel algorithm. In these, we can see the existing trend in the detection of an attacked audio file.  
Test 1. Audio files with possible audio file attacks.  
From Tables 2-6, where the audio files were attacked by other audio files, we observe that the analysis carried out by AQM without having previously processed the signal by the Goertzel algorithm presents a performance of 44.87%, unlike its counterpart with 100% performance.  
To similarly validate the performance of the proposal, AQM tests were carried out for the second block of tests.  
Test 2. Attacked audio files with images or text.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test** & **AQM** & **Without Goertzel Algorithm** & **With Goertzel Algorithm** \\ \hline \multirow{8}{*}{1} & SNR & & \(\blacktriangledown\) \\  & CDZ & \(\blacktriangledown\) & \(\blacktriangledown\) \\  & LLR & \(\blacktriangledown\) & \(\blacktriangledown\) \\  & LAR & & \(\blacktriangledown\) \\  & ISD & & \(\blacktriangledown\) \\  & COSH & & \(\blacktriangledown\) \\  & CD & \(\blacktriangledown\) & \(\blacktriangledown\) \\  & STFRT & \(\blacktriangledown\) & \(\blacktriangledown\) \\  & SP & \(\blacktriangledown\) & \(\blacktriangledown\) \\  & SPM & \(\blacktriangledown\) & \(\blacktriangledown\) \\  & BSD & & \(\blacktriangledown\) \\  & MBSD & \(\blacktriangledown\) & \(\blacktriangledown\) \\  & WSSD & & \(\blacktriangledown\) \\ \hline Hit & & 7/13 & 13/13 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results obtained through AQM for audio files with inserted audio files Test 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test** & **AQM** & **Without Goertzel Algorithm** & **With Goertzel Algorithm** \\ \hline \hline \multirow{8}{*}{4} & SNR & & \\  & CDZ &  
We perform an additional comparison of the proposed method against some works related to steganalysis on audio vectors; we find some applying convolutional neural networks. In Table 12, we show a comparison of the performance of the stegoanalyzers found in the reviewed antecedents. From this comparison we observe that the proposed method has a performance result higher than that found in the literature.  
In Table 13 we show the results obtained in performance of processing time. In Table 13, we present the average processing time for each test performed. Obtaining a total processing average of 0.278 seconds.