AI-Driven Adaptive Noise Cancellation for Hearing Aids

Author(s)	Mr. SURAJ PRABHAKAR GAIKWAD
Country	India
Abstract	Modern hearing aids have improved a lot, but they still face problems in noisy and changing environments. Traditional noise-reduction methods like spectral subtraction or fixed beamforming work only in easy situations. When the noise changes fast or many people are talking at the same time, these methods do not perform well. Because of this, many users say that they can hear the sound, but they cannot clearly understand the speech. In this paper, I propose a new AI-based adaptive noise-cancellation (ANC) system designed for low-power hearing-aid devices. The method uses a small Convolutional Neural Network (CNN) with an attention mechanism to separate speech from noise in real time. I compare this AI model with two common algorithms the Multi-channel Wiener Filter (MWF) and the MVDR beamformer. Simulation results show that the AI system can improve SNR by up to 12 dB and increase speech understanding by around 45% in difficult sound conditions. These results suggest that future hearing aids can offer a more natural, clear, and comfortable listening experience.
Keywords	Adaptive Noise Cancellation, Hearing Aids, Deep Learning, Speech Enhancement
Field	Engineering
Published In	Volume 3, Issue 6, November-December 2021
Published On	2021-11-18

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