What is Digital Signal Processing?

What is Digital Signal Processing?

Digital signal processing is the newest technology in the tech world. It is not as new as the iPod but enough for being a young discipline in different areas such as electronics and computing. Digital Signal Processing a.k.a. (DSP) is widely used in the world as many technologies depend of their algorithms.
Digital signal processing has many applications being the core goals measuring, filtering and compressing real analog signals. These kinds of algorithms depend widely on other systems such as ADC (analog to digital converter) and DAC (digital to analog converter).
Common fields in DSP include audio analysis,speech recognition, sonar, radar processing, spectral estimation, digital image processing, signal processing for communications, control of systems and much more.
DSP algorithms are time consuming for common processors. Therefore, they are widely used on special hardware commonly named as digital signal processors. These are application-specific integrated circuits specifically designed to carry out tasks with improved timing. Nowadays, we can find even more technologies, which are used in the field signal processing including more powerful microprocessors, field-programmable gate arrays (FPGAs), digital signal controllers or commonly know has micro controllers and others.

Learning Digital Signal Processing algorithms

In the field of DSP, it is studied digital signals intodifferent ways: signals in time domain or known as one-dimensional signals, spatial domain (signals with several layers, frequency domain, autocorrelation domain and wavelet domain. It is common to transform each signal to another domain. For example, the most common transformation made in DSP is from the time domain to frequency. This can be done using the Fourier transform and the Fast Fourier transforms.

Implementing DSP Algorithms

As a mentioned before, DSP is often implemented in specialized hardware, which greatly improves fixed-point arithmetic and floating point arithmetic being the last a lot more powerful. For faster applications, it used the FPGA. During the beginning of 2007, multicore implementations of DSPs have emerged. These new features allow even more tasks thus allowing more complex technologies.
I have to mention that DSP is not only limited to specialized hardware. It is also widely used on software applications. For example, Adobe Photoshop uses this technology for their image processing functions. DAW systems or digital audio stations use DSP algorithms as well for all related to audio processing.

Relationship with AI and neural networks

DSP and artificial neural networks are widely related. Both algorithms and implementations can be used for signal processing. Furthermore, these new technologies help each other for proper functioning. For example, it is common to “clean” the input of a neural network with DSP algorithms.