Spectral Analysis
Spectral analysis is a signal processing technique that decomposes a signal into its constituent frequency components to analyze its spectral content. It is widely used in fields like audio processing, telecommunications, and scientific data analysis to identify periodicities, detect patterns, and characterize signals in the frequency domain. Common methods include Fourier transforms, wavelet transforms, and power spectral density estimation.
Developers should learn spectral analysis when working with time-series data, audio/video processing, or any domain involving signal interpretation, such as in IoT sensor analysis, financial time-series forecasting, or biomedical signal processing. It enables tasks like noise reduction, feature extraction, and anomaly detection by revealing hidden frequency-based patterns not apparent in the time domain.