Noisereduction

Noisereduction is a field in signal processing that aims to remove or reduce unwanted random variations in signals produced by electronic devices, sensors, or environments. It seeks to improve clarity, legibility, or intelligibility while preserving the useful information content of the original signal. It is used in consumer electronics, communications, and professional media.

In audio, techniques include spectral subtraction, adaptive filtering, Wiener filtering, Kalman filtering, and noise gates. Modern systems may combine several methods and use machine learning to model noise distributions. They can operate on single-channel recordings or exploit multi-microphone arrays to perform beamforming and spatial denoising.

For images and video, denoising relies on spatial and temporal filtering, non-local means, wavelet or BM3D approaches, and deep learning models. The goal is to suppress random grain and sensor noise while preserving edges and texture. Noise models may account for camera characteristics, ISO level, and motion.

Evaluation typically uses objective metrics such as SNR, PSNR, or perceptual scores like PESQ or STOI for speech, as well as human listening tests. Practical challenges include avoiding artifacts such as musical noise or blurring, managing latency for real-time use, and generalizing to changing noise types.

In practice, noise reduction involves trade-offs between aggressiveness and artifacts, and often requires tuning for the specific application, environment, and user preferences. Advancements in deep learning and multimodal sensing continue to improve robustness and quality.