# Acoustic Material Monitoring in Harsh Steelplant Environments
## 3 Data Acquisition and Preprocessing Methods
### Dataset  
The dataset was acquired using the previously mentioned measurement setup. In the following, the obtained dataset is summarized through exploratory data analysis. A total of 1030 sequences for five different materials with a length of 10 s were recorded each at a sampling rate of 8 kHz and used for model training and method comparison. Hence, 206 sequences per material class were available for evaluation. In addition to that, a separate robustness test dataset with 100 sequences per material class containing adversarial dropping sound and the material sounds were recorded. The goal of this work is to reliably distinguish between five material classes of different hardness. The five materials with the corresponding Mohs hardness scale are listed in Table 1.  
The recordings showed a variability due to different material feed rates, which is seen in Figure 2 from recordings of the same material class.  
The respective signal power of the audio dataset in Figure 3 was computed over whole 10 s signal.  
In addition to that, the RMS value over the dataset was computed on frames of 25 ms length and the normalized histogram over bins of different RMS values was used in order to compare softer and harder materials. In Figure 4, the histograms for softer and harder materials are illustrated. It was observed that softer materials tend to have lower RMS values around 0.12 and harder materials higher RMS values around 0.2. The distribution of the RMS values for softer materials are Gaussian distributed with a spread of 0.06 and normalized occurance of highest RMS value of about 0.09, where as FeSi and slag have of about 0.13 to 0.24 indicating that the audio signals from softer materials contain characteristic dropping sound with impulses of smaller magnitudes.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**FeSi Br.** & **FeSi Lumps** & **Lime** & **Magnesite** & **Slag** \\ \hline
6.5 [35] & 6.5 [35] & 3–4 [36] & 3.5–4 [37] & 5–6 [38] \\ \hline \end{tabular}
\end{table}
Table 1: Conveyed Material and their corresponding Mohs hardness scale.  
Figure 2: Different feed rates for same material class.  
The rise time over the whole dataset for characteristic dropping sounds for these materials was computed. The violin-plot of rise times from Figure 5 shows that harder materials have a lower rise time than softer materials, indicating that the impulses of the characteristic sound of harder materials are sharper than softer materials. This is explained because of the more granular and solid consistency of the hard materials.  
Based on that, slag, FeSi Br and FeSi Lump contain more characteristic short-term instationarities with high frequencies than magnesite and lime. In Figure 6, the spectrograms for characteristic 10 s recordings for the different materials are shown. The characteristic short-term instationarities are illustrated through the characteristic spread in a frequency range of 1500-2500 Hz, as shown in the first, second and fifth spectrogram of FeSi Br, FeSi Lump and slag from Figure 6.  
Figure 4: RMS of the dataset computed on frames of 25 ms width: (**left**) softer materials and (**right**) harder materials.  
Figure 3: Variability of the dataset: Different material feed rates and classes.  
Figure 5: Rise times of characteristic dropping sound for different materials.  
Figure 6: Spectrograms of typical audio signals for different material classes of length 10 s and window width \(h=2048\).  
Based on these findings, it can be confirmed that the acoustic signals of the different materials have specific characteristic patterns for distinguishing between them and are sufficiently represented in the dataset. It was shown that the patterns are not discriminative enough for simple classification. Hence, in the following, a deep-learning-based classification approach is used.