## 1 Introduction and Motivation  
The World Health Organization (WHO) estimates that by 2050, nearly 2.5 billion people are projected to have some degree of hearing loss, which poses an annual global cost of US 5980 billion [1]. [PERSON], a WHO Senior Information Officer, wrote an article to make a call for the private sector to provide affordable hearing aids in developing countries (as their current cost ranges from US $200 to over US $500) [2]. In addition, the Healthline Organization reported that a set of hearing aids might cost $5000 [3]. The main components of a digital hearing aid are shown in Figure 1, comprising of a microphone, an analogue to digital converter (A/D), filter banks, gain blocks, and a digital to analogue converter (D/A). First, the analogue sound signal detected by the microphone is converted into digital form by an A/D converter. Next, this digital signal is applied to a filter bank. Different digital signal theories can be applied to divide the input digitized sound signal spectrum into sub-bands with different bandwidths. Then, gain blocks are applied to the outputs of the filter bank to amplify the sound signal to the desired hearing level. In the last stage, the signal is converted from digital to analogue by the D/A converter [4; 5]. However, it is better to design digital filters that can match multiple audiograms for patients who suffer from hearing loss. This approach lowers the cost of manufacturing hearing aids as they can be produced on a large scale as designed by many research techniques [4; 6; 7; 8; 9]. On the other hand, it increases the complexity of hearing aid design, requiring high operating power and big chip area, leading to improperly fitted hearing aid [10]. This research was motivated by all these considerations in hearing aid design and the impact of hearing loss on nations' economies and patients' ability to afford the hearing aid. The main idea here is to use artificial intelligence and machine learning to facilitate the whole process for the patients and the hearing aid designers. In addition, the process of fitting hearing aids is tiring and consuming in time as it depends on many trials which require the patient to be highly responsive. A study stated that only 50-60% are satisfied with their hearing aids use [11]. Furthermore, integrating all these factors with the severe shortage of numbers of audiologists who are very rare and hard to find in rural areas [12; 13]. This urges the use of artificial intelligence new technology to resolve these problems, especially when the fitting process depends on the skills and experience of audiologists [14]. In this work, the authors apply unsupervised learning to cluster audiograms using spectral clustering. These audiograms are taken from a database of 28,244 audiograms used by [PERSON], [PERSON] and [PERSON] [15] to produce a standard set of audiograms for the IEC (International Electrotechnical Commission) 60118-15 measurement procedure. These audiograms were clustered by vector quantization analysis of size 60. Here, the researchers excluded five audiograms of the quantized results, representing normal hearing levels (0-20 dB) defined by different health organizations [1; 16; 17]. These five audiograms are removed since the study aims to produce clusters representing different audiograms for  
Figure 1: Hearing aid main stages. patients who experience hearing loss. Different audiograms with the same shape but with different levels can be realized with the same set of filters by adjusting the gains to match the required audiogram shape. Another reason to classify audiograms according to shape is, fitting hearing aid process will be easier. A supervised machine learning model can be built based on this work to classify patients' audiograms then program or adjust hearing aid according to pre-set configurations. These configurations are linked to the clusters produced by this work then at the end of the process of hearing aid fitting, a fine tuning might be needed.