# Acoustic Material Monitoring in Harsh Steelplant Environments
## 2 Literature Review
Authors of [25] concluded that the application of automatic feature extraction with CNNs might be a promising way for further investigations. The results from [6; 25] suggested that the BPSD-features had the strongest classification power. Hence, BPSD is used within this work. In both studies [6; 25] the classifiers were compared with the same test dataset not considering adversarial anomalies. For acoustic classification, it is important that the dataset should be properly cleaned and preprocessed. Due to harsh environmental conditions in steel plants, audio recordings are often perturbed with noise, such as impulse noises, which are not indicative of the transported material. Investigations of the dataset showed that audio recordings are corrupted with impulsive short-term non-stationarities, which are not characteristic of the material sound. These non-stationarities are caused by surrounding effects such as dropping sounds from different environmental contaminants and hence the acoustic monitoring system has an inherent risk of misclassification. Typically, these environmental contaminations are caused by sounds of dropping hopper wear plates and other environmental inclusions. The contaminants can remain inside the material hopper for longer and are larger than the conveyed materials. These larger blocks break apart and generate a sequence of impulses. An enhanced sensitivity range yielding a corresponding endpoint extension is therefore required as explained in Section 3. To separate uninformative loud noises from informative content of the signal, a signal-analysis-based anomaly detection method is required. Anomaly detection for signals is a research topic for many industries. Different anomaly detection algorithms have been introduced in [26]. In [27], predictive AR-models were used to predict the corresponding outliers and showed promising results with regard to the detection accuracy. The coefficients and model order of autoregressive (AR) models highly depend on the stationarity of the signal and corresponding process. With the increase of computational power and data, deep learning methods such as autoencoders, time-series-based CNNs and LSTMs achieved representative results for anomaly detection [28; 29]. These approaches require precisely labelled training data consisting of both normal and anomalous signals or a significant amount of normal signals [30]. Therefore, dataset preparation and acquisition is very time-consuming [31]. On the other hand, many outlier detector methods, which require less parameter selection and training time, were proposed [32]. In [33], the requirement for variable anomaly detection width for real recorded sounds was illustrated. The selection of the appropriate anomaly detection width is a tradeoff. An excessively small detection width leads to imprecise removal of anomalies, whereas a very wide detection width leads to discarding the informative content of the signal. Hence, this article proposes the RMSE-detector for audio signal preprocessing. As previously stated, psychoacoustic features are commonly used for ESC. In [6], different psycho-acoustic features were compared. Referring to previous explanations, this work will employ BPSD-features as inputs for the classification models. Therefore, the scope of the study is set to robust classification with RMSE-preprocessing in combination with BPSDs and two-dimensional CNNs.