/ˈdɪdʒɪtl ˈsɪgnəl ˈprəʊsɛsɪŋ/
noun — "analyzing and modifying signals with algorithms."
Digital Signal Processing, often abbreviated as DSP, is the mathematical and computational manipulation of digital signals to extract information, improve quality, or enable desired transformations. It involves the use of algorithms to process sampled data from analog signals that have been converted to digital form via an analog-to-digital converter (ADC). DSP is fundamental in telecommunications, audio and video processing, biomedical instrumentation, radar systems, and embedded electronics.
Technically, DSP algorithms operate on discrete-time signals, performing operations such as filtering, Fourier transforms, convolution, correlation, modulation, and compression. Systems implementing DSP can be realized in software on general-purpose processors, in specialized DSP processors, or in hardware using FPGAs and ASICs for high-speed applications. Precision, sampling rate, and computational efficiency are key considerations, as these factors affect signal fidelity and system performance.
# Example: simple digital low-pass filter (conceptual)
input_signal = [x0, x1, x2, x3, ...]
output_signal[0] = input_signal[0]
for n in 1..N:
output_signal[n] = 0.5 * input_signal[n] + 0.5 * output_signal[n-1]
# applies smoothing to high-frequency variations
In embedded workflows, DSP is used to:
- Enhance audio signals in speakers or headphones
- Filter noise from sensor measurements
- Compress video streams for transmission
- Detect patterns in radar or medical imaging signals
Conceptually, DSP is like a digital craftsman shaping and refining signals. Raw measurements are transformed into cleaner, more usable, or more meaningful forms by applying mathematical tools and algorithms. Whether isolating a voice from background noise, compressing a video without losing detail, or detecting a heartbeat pattern, DSP makes precise, reliable signal manipulation possible in digital systems.
See FPGA, ASIC, Embedded Systems, ADC, Filter.