Implementation of DSP Techniques for Noise Cancellation in Audio Signals

Abhinav Hiremath

Independent Researcher

India

Abstract
This manuscript examines the implementation of digital signal processing (DSP) techniques for noise cancellation in audio signals, focusing exclusively on methods and technologies available up to and including 2018. It reviews classical approaches such as spectral subtraction, Wiener filtering, and adaptive filtering algorithms—particularly the least mean squares (LMS) and recursive least squares (RLS) methods—and evaluates their efficacy in various noise environments. Case studies from telephony, hearing aids, and automotive audio systems illustrate real‐world performance. A detailed methodology section describes design considerations, algorithm selection, and simulation framework. Results demonstrate that, when rigorously configured, these DSP techniques can achieve up to 20 dB noise reduction in stationary noise and 10 dB in nonstationary environments without introducing perceptible distortion. The conclusion summarizes best practices and identifies areas for further optimization within pre-2018 technological constraints.

Keywords
DSP, Noise Cancellation, Spectral Subtraction, Wiener Filter, LMS Adaptive Filtering, RLS, Audio Signal Processing

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