FPGA-based Impulsive Adaptive Filter for Audio Noise Suppression
Keywords:
Adaptive filtering, Audio denoising, FPGA implementation, IM-VRLS algorithm, Mean Square Error (MSE), Real-time signal processing, Signal-to-Noise Ratio (SNR)Abstract
Environmental noise significantly affects the quality and intelligibility of audio signals in modern communication, multimedia, and recording systems. Effective noise suppression techniques are therefore essential to ensure reliable audio transmission and improved listening experiences. Adaptive filtering has emerged as a widely used solution for audio denoising due to its ability to adjust filter parameters in response to changing noise environments. However, conventional adaptive filtering algorithms such as Least Mean Squares (LMS) and Recursive Least Squares (RLS) often encounter limitations related to convergence speed, stability, and computational performance. To overcome these challenges, this work presents the design and FPGA-based implementation of an adaptive audio denoising system using the Impulsive-Metric Variable Regularized Least Squares (IM-VRLS) algorithm. The proposed algorithm employs an exponential error-dependent step-size adaptation mechanism together with a Kalman gain-based weight update strategy to improve filtering accuracy and convergence behavior. The system is implemented on an FPGA platform to evaluate its real-time processing capability and hardware efficiency. Performance is analyzed using Signal-to-Noise Ratio (SNR), Mean Squared Error (MSE), and convergence rate metrics. Experimental results demonstrate that the IM-VRLS algorithm provides superior noise suppression, lower estimation error, and faster convergence compared with conventional LMS and RLS algorithms. Real-time audio streaming through line-in and line-out interfaces further validates the practical applicability of the proposed design. The successful FPGA implementation confirms that the proposed IM-VRLS-based adaptive filter is an efficient and reliable solution for real-time audio denoising applications.
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