Signalverarbeitungsschaltung

Signal processing, known in German as Signalverarbeitung, is the discipline concerned with analyzing, modifying, and interpreting signals such as audio, visual, spatial, or sensor data. It encompasses techniques for extracting information, reducing noise, compressing data, and reconstructing signals after transmission or storage. Broadly, the field covers both analog (continuous-time) and digital (discrete-time) processing, with digital methods dominating many modern applications due to flexibility and computational power.

Key concepts include models of signals and systems, linearity, and time-invariance. In the time domain, processing often uses convolution with impulse responses and filtering. In the frequency domain, transforms such as the Fourier transform and its discrete form (FFT) reveal spectral content and enable efficient design of filters. The sampling theorem describes how continuous-time signals can be represented by discrete samples, highlighting issues of aliasing and quantization.

Digital signal processing implements algorithms on computers or specialized hardware. Common building blocks include finite impulse response (FIR) and infinite impulse response (IIR) filters, spectral analysis tools, adaptive filters, and data compression algorithms. Implementations may run on general processors, digital signal processors, field-programmable gate arrays, or embedded systems, balancing precision, latency, and power consumption.

Applications span communications, audio and speech processing, image and video processing, biomedical engineering, radar and sonar, geophysics, and instrumentation. The field continues to evolve alongside advances in sampling hardware, software tools, and information theory, integrating traditional signal-processing techniques with modern data-driven approaches when appropriate.