Signal Processing

Computer audition is the ability of computers to analyze and interpret audio signals.

Deconvolution is a mathematical technique used to reverse the effects of convolution on data, often applied in signal and image processing.

The Discrete Fourier Transform (DFT) converts a sequence of values into components of different frequencies.

Discrete time refers to a type of signal or system that is analyzed at distinct intervals rather than continuously.

An Extended Kalman Filter is an algorithm used for estimating the state of a nonlinear dynamic system.

Fast Fourier Transform (FFT) is an efficient algorithm to compute the Fourier Transform of a signal.

A filter bank is a collection of filters used to process signals by decomposing them into various frequency components.

Fourier Analysis studies how functions can be expressed as sums of sinusoidal components.

A Fourier series represents a periodic function as a sum of sine and cosine functions.

The Fourier Transform converts signals between time and frequency domains, revealing frequency components in data.

A mathematical transformation that generalizes the Fourier Transform, representing signals in fractional frequency components.

The frequency domain represents signals in terms of their frequency components rather than time.

Graph Signal Processing (GSP) analyzes signals defined on graphs, extending traditional signal processing concepts to networked data.

Impulse response is how a system reacts to a brief input signal, revealing its characteristics and behavior.

A computational technique to separate a multivariate signal into additive, independent components.

Intensity normalization adjusts data values to a common scale for better comparison and analysis.

Kalman Gain is a factor used in the Kalman filter that balances the weight of new measurements and predictions.

Log-Domain Computation refers to mathematical operations performed in the logarithmic scale for efficiency and stability.

A median filter is a non-linear digital filtering technique used to remove noise from images or signals.

Min-SNR stands for Minimum Signal-to-Noise Ratio, a measure of signal quality in communication systems.

Multi-path fading is a phenomenon in wireless communication where signals reach the receiver via multiple paths, causing fluctuations in signal strength.

Multi-scale features refer to patterns and information extracted from data at different scales or resolutions.

Noise filtering is a technique used to remove unwanted noise from data or signals to improve clarity and accuracy.

The noise floor is the level of background noise in a system, affecting signal clarity and quality.

A noisy signal is a signal that contains unwanted disturbances or random variations, affecting data quality.

Non-linear filters process signals or images by applying non-linear operations to reduce noise or enhance features.

A normalized signal scales data to a common range for improved analysis and processing in various applications.

An optimal filter is a mathematical approach used to minimize noise and improve signal quality in data processing.