## 2 Related Work
[PERSON] [22], has classified audiograms based on UKHSE (United Kingdom Health and Safety Executive) categorization scheme. The sum of pure tone audiometry test hearing levels at frequencies 1 kHz, 2 kHz, 3 kHz, 4 kHz and 6 kHz, was obtained. Then, compared with the figures set by UKHSE and classified as one of the following: acceptable hearing ability, mild hearing impairment, poor hearing, or rapid hearing loss. The aim of this classification was to prompt proper actions to prevent noise-induced hearing loss. The annotation process was carried out by experts in the field who applied the UKHSE standards. [PERSON] and his team [23] made a longitudinal study on how the shape of audiograms change over time. The follow up was carried out based on four stages; 1993-1995, 1998-2000, 2003-2005, and 2009-2010. The audiograms were classified into eight levels and the change in hearing ability over time was recorded based on these classes. [PERSON] and [PERSON] [22; 23] didn't implement any intelligent solutions as they counted on the experience of the specialists in the field. Classifier techniques found in the literature, are summarized in Table 1, showing the limitations and short-coming of each technique. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & \multicolumn{2}{c}{**Classification Technique**} & **Limitations** \\ \hline \multirow{8}{*}{[PERSON] et al. [19]} & a. & It is a two-stage classifier. & \multirow{8}{*}{a.} & \multirow{8}{*}{It used different types of hearing tests, not only audiograms, to classify data to detect type of hearing loss.} \\  & b. & The first stage is unsupervised & & \\  & machine learning then followed by & & & \\  & supervised learning. & & & \\  & c. & It uses different hearing tests to & & \\  & classify hearing loss into 4 types & & & \\  & related to sensitivity and clarity loss. & & & \\ \hline \multirow{8}{*}{[PERSON] [20]} & a. & It is a classifier to audiograms with two steps; the first step is & & \\  & unsupervised learning to cluster & & & \\  & audiograms into 4 pre-set & & & \\  & configurations for a hearing aid. & & & \\  & b. & Second, audiograms are mapped to & & \\  & these 4 configurations with & & & \\  & supervised learning. & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of audiogram classifiers. To the best of our knowledge, the classifiers that are built with the purpose to classify audiograms are very few and not suitable as a defence to the specialists in the field, such as audiologists, hearing aid specialists, and hearing aid designers. This study is the first study to classify audiograms according to the similarity in shape with the aim to reduce the complexity of the filter bank used to realize the audiogram shape of the patients. According to signal processing techniques, it is important to know the shape of the audiogram to apply different gains to different filters that cover the entire band of hearing (125 Hz-8 KHz). This classifier is built to capture different shapes of audiograms and not to classify hearing loss type as achieved by current existing works. Audiograms of similar shape at different levels can be realized by a group of filters by changing the gain coefficients of each filter or the overall gain of the cascaded filters. This classification will help hearing aid designers to reduce the complexity of their filter designs and can be a good start for the future supervised learning algorithm to classify audiograms according to these detected shapes. Applying novel methods such as sophisticated machine learning algorithm will facilitate the whole process for the experts and to increase patients' satisfaction.