: Measures the "peakedness" of the signal distribution to identify transients or noise spikes.
In Digital Signal Processing (DSP), is the process of transforming raw signal data into a reduced set of meaningful values that represent the signal's essential characteristics . This is a critical step for tasks like speech recognition, audio classification, and anomaly detection. Common Feature Categories in DSP : Measures the "peakedness" of the signal distribution
: Represents the "center of mass" of the spectrum, often associated with the perceived "brightness" of a sound. Common Feature Categories in DSP : Represents the
: Indicates the asymmetry of the signal's distribution. Steps to Create a Feature Extraction Module Depending
: Quantifies the level of a desired signal compared to the background noise. Steps to Create a Feature Extraction Module
Depending on the application, you can "create" or extract features from different domains:
: A standard set of features for speech and music processing that mimics human hearing.