Additive Gaussian Noise
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Resource Overview
Gaussian Noise Addition Implementation
Detailed Documentation
Adding Gaussian noise to signals is a common technique for simulating real-world noise interference. Gaussian noise possesses random characteristics with statistical properties that follow a Gaussian distribution. By incorporating Gaussian noise into signals, it becomes possible to emulate various noise interference scenarios encountered in practical environments, thereby enabling more accurate evaluation of signal processing algorithms' performance and robustness.
Implementation typically involves generating Gaussian-distributed random values using functions like numpy.random.normal() in Python or randn() in MATLAB, which requires specifying the noise level through variance or standard deviation parameters. The noise addition process follows the simple arithmetic operation: noisy_signal = original_signal + noise_vector, where the noise vector length must match the signal dimension. For optimal implementation, the signal-to-noise ratio (SNR) should be controlled programmatically to ensure consistent testing conditions across different signal types and applications.- Login to Download
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