Digital Signal Processing Basics
DSP stands for Digital Signal Processing, which is a general term for the techniques or algorithms used to handle digital signals from various sources, like audio and speech, video, images, and sensor arrays. Many types of processing can be done on these signals, depending on the application needs. Some examples include noise removal, equalization, echo cancellation, compression and transmission.
Signal processing applications will usually have an analog-to-digital converter (ADC), followed by the digital processing (DSP), possibly followed by a digital-to-analog conversion (DAC). For single-dimensional signal like audio, the ADC will sample the signal repeatedly many times per second, providing the DSP with a stream of numbers that represent the signal digitally. Multi-dimensional signals like video get digitized in space as well as time, providing a large array of numbers many times per second. The DSP needs to process all this data in real time, and produce the output before the next set of data arrives. This is an extremely challenging task that often requires specialized hardware.
Digital Signal Processor Basics
The term DSP is also commonly used to refer to Digital Signal Processors, which are microprocessors that have been specifically designed to handle the high computational complexity of typical DSP algorithms. They achieve this by utilizing special-purpose sets of instructions that help typical DSP algorithms, like multiply-accumulate instructions, instructions that operate on multiple data elements in parallel (SIMD instructions), the ability to issue multiple instructions every cycle (VLIW architectures), streamlined memory access and cache management. Some popular DSP processors are the Texas Instruments TMS320C series, and the Analog Devices BlackFin. These processors are difficult to program, and often require manual optimizations in low level languages to fully exploit their potential.