# Acoustic Material Monitoring in Harsh Steelplant Environments
## 2 Literature Review  
For material-type classification, different sensing principles like physical sensors or soft sensors exist. Soft sensors are based on the principle of indirect estimation of a physical state from another physical state [7], whereby the rough conditions of iron- and steelmaking are often limiting factors in practical applications. A promising soft sensor method is the use of acoustic emission as the sensing principle. In the literature, there are many different application fields where acoustic emission sound has been used to serve as a soft sensor principle. The domain of acoustics ranges from the estimation of rain drop size in the ocean [8], music classification [9; 10], acoustic targets recognition [11], rock micro-fracture classification [12], denoising of bridge structures [13], class room event classification [14] to many other application fields. Over the recent decades, many investigations have been focused on the evaluation of various material properties based on acoustic emission. In [15], acoustic emission was used to analyze phase transformations of steel material. Authors from [16] found a correlation between acoustic features and particle size evaluated in the ultrasound frequency range. Due to the fact that it is possible to distinguish between different materials through acoustic sounds, this research will focus on the application of acoustic-based material classification in the industrial steelplant environment. Over the recent decades, machine learning algorithms have advanced the application fields of acoustic soft sensors. In general, supervised and unsupervised algorithms in machine learning exist. Unsupervised methods are machine learning methods, where the algorithm learns patterns from unlabelled data. Different acoustical applications were addressed with unsupervised learning. In [11], authors were characterizing CFRP laminates using acoustic spectral peak frequencies and amplitudes in combination with k-meansclustering. Authors from [17] applied k-means clustering for acoustic monitoring of an additive manufacturing process. In [18], a Gaussian mixture model (GMM) was used to derive the clusters to control the noise source generation. Authors from [19] summarized the shortcomings of k-means and GMM-clustering: K-means have problems with clusters of different sizes, densities and irregular cluster shape. GMM assumes that input variables are normally distributed and clusters having a specific shape. Both k-means and GMM are limited with the initial cluster parameter. Researchers from [20] used the autoencoder for unsupervised anomalous sound detection and in [21] authors used Self-Organising Maps (SOMs) for unsupervised acoustic monitoring. For unsupervised methods, a significant amount of data is required. Therefore, this research focuses on supervised approaches. In [22; 23; 24], a variety of supervised classification models and feature computation techniques were investigated for ESC. Researchers from [6] introduced a neural network and psychoacoustic features-based classification system for a material transport process on a conveyor belt. Different spectral features such as the Mel Frequency Cepstral Coefficient (MFCC), Bark Scale Power Spectral Density (BPSD), Spectral Sharpness (SS) and Crest Factor (CF) were compared with respect to the overall classification performance. The system could predict the materials with an overall accuracy of 95.4%. In [25], a deep neural network approach based on an autoencoder in combination with a Long Short Term Memory (LSTM) neural network classifier structure for material classification was proposed. Spectral features such as the BPSD, temporal variation of BPSD (DBPSD), temporal variation of the DBPSD (D2 BPSD) and Spectral Sharpness (SS) were used, resulting in a feature input vector of 80 features. With the autoencoder, the number of relevant features was reduced from 80 to 47 spectral features. This led to an improvement of the accuracy to 96.6%.