What Is Digital Signal Processing?

Digital signal processing (DSP) is the use of processing techniques to analyze, transform, and transmit digital signals. Digital signals are bits of information sampled from continuous-time analog signals or produced directly from digital systems. Digital signals are discrete in time and quantized, which allows digital computers to utilize and manipulate information quickly and efficiently.

Key Concepts

• Sampling: The process of converting continuous analog signals into discrete digital signals by measuring the signal at regular intervals.
• Filtering: A fundamental operation in digital signal processing used to eliminate undesired characteristics from signals and enhance their quality to prepare them for further processing. You can use MATLAB ® to design filters.
• Transforms: Digital signal processing uses mathematical transforms such as the discrete Fourier transform (DFT) to enable the analysis and manipulation of signals in the frequency domain. The fast Fourier transform (FFT) is the algorithm used to compute the DFT. Signals can also be analyzed in the time-frequency domain with DFT-based transforms or wavelet-based transforms.

Digital signal processing has applications in fields such as wireless communications, audio and video processing, medical imaging, radar and sonar systems, control systems, and biomedical signal processing. MATLAB, Simulink ® , and add-on products, such as Signal Processing Toolbox™, DSP System Toolbox™, and Radar Toolbox, let you analyze, design, and build digital signal processing systems.

Practical Applications

• Image Processing: Digital signal processing techniques can be applied in two dimensions to images and are used in image processing tasks such as image enhancement, image compression, object recognition, and computer vision.
• Audio Processing: Digital signal processing is extensively used in audio applications, such as noise cancellation, equalization, and speech recognition. Many audio processing techniques require adaptive filtering. Adaptive filters are filters that change over time to account for the received signal. For more information about audio processing, see Audio Toolbox™.
• Communications: Digital signal processing techniques are used for modulation and demodulation of signals in communication systems. Modulation involves encoding information onto a carrier signal, while demodulation is the process of extracting the original information from the modulated signal. Communications channels introduce distortion and noise that affect the transmitted signal. Received signals must be filtered and processed to account for the distortion introduced to the signal. For more information about communications, see Communications Toolbox™.

Examples and How To

• Signal Processing Onramp - Course
• Filter Design in MATLAB - Example
• Active Noise Control – From Modeling to Real-Time Prototyping (7:16) - Video
• Transform Time-Domain Data into Frequency Domain - Example
• Filter Design Gallery - Example
• Introduction to Streaming Signal Processing in MATLAB - Example