Implementation of Music Noise Removal Technology based on Adaptive Filtering Algorithm

This article explores a real-time parameter adjustment noise reduction technology to enhance music quality in varying background noise environments. It constructs a diverse audio dataset and converts time-domain signals to the frequency domain for processing. Utilizing LMS(Least-Mean-Square) and RLS (Recursive Least Square) adaptive filtering algorithms, it optimizes parameters based on different noise environments. An adaptive mechanism is developed to automatically adjust filter parameters according to noise characteristics. Experiments demonstrate significant improvements, with an average signal-to-noise ratio of 28.03dB, a noise suppression rate of 81.04%, and an average sound quality score of 8.85. This method effectively reduces noise while preserving music's original emotion and details, offering superior audio quality and user experience in dynamic noise environments. The proposed solution showcases feasibility and superiority, providing a novel approach for audio processing.