Introduction: Hearing impairment is a growing global issue due to aging populations and noise pollution. AI offers innovative solutions for diagnosing and treating hearing impairments and enhancing hearing devices. This research leverages AI to tackle key challenges in auditory science, improving hearing health.
Background: Hearing loss, often caused by noise, medications, genetics, or infections, is traditionally diagnosed through clinical tests that are not always accessible. AI, especially through machine learning (ML) and deep learning (DL), is transforming hearing health by enabling advanced data analysis, diagnostics, and device improvements.
Objectives: This study aims to enhance diagnostic accuracy using ML on hearing test data, optimize hearing devices with DL for better performance in complex environments, and develop smartphone applications for remote hearing health monitoring.
Content: The research includes building ML models to analyze hearing data, applying DL for noise suppression and speech recognition in devices, and integrating AI with apps for remote monitoring and diagnosis.
Methods: Data collection and preprocessing will involve dimensionality reduction and feature selection for model efficiency. The development and testing of ML and DL models will be conducted using Python and TensorFlow, followed by integration into devices and extensive user testing.
Outcomes: The research seeks to create an AI-based diagnostic system, improve hearing device functionality, and establish a remote monitoring platform for broader accessibility to hearing health services.
Conclusion: By utilizing AI, this research aims to significantly advance auditory science, leading to better diagnostics, optimized hearing devices, and increased access to hearing health care globally.