## 2 Related Work
### Pure-Tone Audometry  
PTA is the most fundamental and widely used hearing assessment for evaluating the degree and type of hearing loss. This test generates pure tones at various frequency ranges and measures the hearing threshold--the minimum sound intensity a patient can perceive [18]. Commonly tested frequencies range from 125 Hz to 8000 Hz, including 250 Hz, 500 Hz, 1000 Hz, 2000 Hz, and 4000 Hz, allowing for a detailed assessment of hearing loss across different frequencies [19]. Using an audiometer, the sound intensity is gradually adjusted and the patient responds when they perceive the sound, enabling the determination of their hearing threshold [20]. The test is conducted independently for each ear using either headphones or a BC vibrator, and the results are typically visualized in an audiogram. PTA is categorized into AC testing and BC testing based on the sound transmission pathway. AC testing evaluates sound transmission through the external and middle ear, making it helpful in identifying conductive hearing loss caused by outer or middle ear issues [21]. In contrast, BC testing assesses sound transmission through the skull, allowing for the diagnosis of sensorineural hearing loss resulting from inner ear or auditory nerve damage. By comparing the results of these two tests, clinicians can determine whether hearing loss originates from the outer/middle ear or the inner ear/auditory nerve [22]. During the test, patients respond to sounds at different frequencies, enabling precise identification of hearing loss severity. If hearing loss occurs only in specific frequency ranges, it may indicate damage to a particular auditory pathway or an underlying disorder [23]. The results, recorded as an audiogram, comprehensively analyze hearing loss severity and type. This information is crucial for assessing the suitability of hearing assistive devices such as hearing aids or cochlear implants and for designing auditory rehabilitation programs. PTA is a relatively simple, yet essential method for the early detection of hearing loss and plays a critical role in evaluating auditory health and formulating treatment plans [24]. [PERSON], [PERSON] et al. [25] evaluated a deep learning-based approach for classifying hearing loss types using PTA data. The study utilized 4007 PTA records analyzed and labeled by medical experts between 2017 and 2021, comparing various deep learning models. This study employed RNN, LSTM, and GRU, which are well suited for learning sequential data patterns, to analyze PTA data. Additionally, the impact of different normalization techniques, including Z score, MinMaxScaler, and MaxAbsScaler, on model performance was examined. The experimental results demonstrated that the LSTM model achieved the highest classification accuracy of 97.56%, indicating that deep learning models capable of capturing sequential patterns in PTA data are effective for hearing loss classification. Furthermore, this study explored the potential of CGAN-based data augmentation to enhance model performance. By incorporating generated data into the training process, the generalization performance of the models improved, with LSTM and GRU modelsexhibiting the most significant enhancements. These findings highlight that CGAN-based data augmentation can serve as an effective method to enhance deep learning model training, particularly in medical domains with limited data availability  
[PERSON], [PERSON] et al. [26] developed a machine learning model to automatically diagnose [PERSON]'s disease and predict endolymphatic hydrops based on PTA data. Traditionally, a diagnosis of [PERSON]'s disease has relied on gadolinium-enhanced magnetic resonance imaging and hearing tests; however, magnetic resonance imaging is costly and has limited accessibility. To address this issue, this study proposed a more efficient diagnostic approach by training a machine learning model using pure-tone audiometry data.