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Gaussian noise is a statistical noise having a probability density function equal to that of the normal distribution, often used in signal processing to simulate real-world random variations. It is characterized by its mean and variance, and is commonly assumed in many algorithms due to the central limit theorem, which suggests that the sum of many independent random variables tends toward a Gaussian distribution.
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White noise is a random signal having equal intensity at different frequencies, giving it a constant power spectral density. It's often used in various fields, including acoustics, electronics, and statistics, to model random processes or to test systems and algorithms.
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Signal-to-Noise Ratio (SNR) is a measure used to compare the level of a desired signal to the level of background noise, often expressed in decibels. A higher SNR indicates a clearer and more distinguishable signal, which is crucial for effective communication and data processing in various fields such as telecommunications and audio engineering.
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Autocorrelation measures the correlation of a signal with a delayed version of itself, often used to identify repeating patterns or trends in time series data. It is crucial for understanding the internal structure of data and can indicate whether the assumption of independence in statistical models is valid.
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Power spectral density (PSD) is a measure of a signal's power intensity in the frequency domain, providing insights into how power is distributed across different frequency components. It is essential for analyzing signals in various fields like telecommunications, audio processing, and vibration analysis, helping to identify dominant frequencies and noise levels.
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Noise filtering is the process of removing unwanted or irrelevant data from a signal or dataset to enhance the quality and reliability of the information. It is crucial in various fields like telecommunications, data science, and audio processing to improve accuracy and performance.
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Stochastic processes are mathematical objects used to model systems that evolve over time with inherent randomness. They are essential in various fields such as finance, physics, and biology for predicting and understanding complex systems where outcomes are uncertain.
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Spectral Subtraction is a noise reduction technique commonly used in audio signal processing to enhance speech signals by estimating and subtracting the noise spectrum from the noisy signal's spectrum. It is particularly effective in environments with stationary noise, but can introduce artifacts known as 'musical noise' if not carefully implemented.
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