dsp

Digital signal processing (DSP) is the field of manipulating signals that have been converted to a digital representation. DSP algorithms operate on discrete-time samples rather than continuous waveforms, enabling tasks such as filtering, spectral analysis, and data compression. The practice relies on the sampling theorem and quantization, which define how continuous signals are captured as digital data and how precision affects processing results.

Core techniques include digital filters (finite impulse response FIR and infinite impulse response IIR), convolution, and frequency-domain methods such as fast Fourier transform FFT. Other common operations are denoising, equalization, echo and reverb, compression, modulation, and demodulation. Adaptive methods adjust filter parameters in response to changing input characteristics. DSP also covers spectrum estimation, feature extraction, and digitization of control loops for embedded systems.

Implementation can be on dedicated DSP processors, general-purpose CPUs with vector instructions, or programmable hardware such as FPGAs and ASICs. Real-time performance, power consumption, and fixed- or floating-point arithmetic influence design choices. Software tools and environments provide simulation, prototyping, and deployment paths, often using modular blocks for filters, transforms, and codecs.

Applications span audio processing, telecommunications, image and video processing, biomedical signal analysis, radar and sonar, wireless networking, and control systems. In consumer electronics, DSP powers music playback, voice processing, and hearing aids. In science and engineering, DSP supports seismic data interpretation, remote sensing, and experimental instrumentation. The field continues to evolve with advances in parallel processing, machine learning integration, and specialized hardware.