Well Ideally the application is defined for the signal you are trying to process. It can be anything from audio, video, sensor output, data from the web, in short and simple words any sort of information. So processing it means making the information understandable i.e. like how discrete fourier transforms are used to understand the frequency components of a signal.

Ideally DSP is thought to be about 1-D (audio, sensor output), 2-D (images and video) signal processing. Currently the hottest areas/applications in Digital Signal Processing are:

· Compression: Any sort of data that can be stored in as few bits as possible with varying degrees of recoverability. Lossy or lossless.

· Communication and Audio: Noise suppression/removal or speech enhancement(for audio) in these signals is always there. Plus for music applications, pushing the envelope for audiophiles is an ever present market. Better sound processors for instruments is also there.

· Image and Video: This is by far the hottest market for DSP. Since you're smartphones got cameras, every company is just about megapixels, low-light clarity, HDR and all sorts of image enhancement algorithms. With the introduction of 4K video, compression is an even higher priority than before. Plus Medical imaging is the epitome of this specific sub field of DSP.

· Bio-Sensors- Now you have pedometers on your smartphones, as wrist bands and as wearables. Measuring your heart rate with your smartphone camera. TI came out with a prototype to measure your SPO2 on a wristwatch. All these biometric applications are key to making healthcare more mobile for consumers

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All these things are there in DSP. And this is just the broad applications of it. MIT is trying to extract audio by looking at the vibrations in objects

And it can be done for Data Science as well. Statistical signal processing is a growing field and if you have an innate talent for statistics and like signal processing, this is the correct time to push the field for it.

Digital signal processing has a wide variety of applications, including:

· Audio and video compression (the quality depends on the sampling rate chosen - higher sampling rate = higher quality. The file size can be compressed by applying source coding, such as Huffman coding.)

· Audio signal processing (example: applying a low pass or bandpass filter to reduce external noise from an audio recording)

· Image processing (example: using FFT, filtering and inverse FFT in order to remove noise from an image)

· Medical applications (example: applying a histogram equalization to enhance an x-ray image)

Digital Signal Processing is a very vast and interesting topic in its own. In short, as the name suggests, its the process of analyzing, processing and transforming the analog signals into digital signals.

In other words, if one form of signal is processed electronically and produced as another signal, then the process is called as Digital Signal Processing.

This topic typically falls under electronics and computer engineering, however, with the advent of technology, almost every electronic device uses this technology.

If we consider any uniform and stable sine wave, the computer registers different positions of the wave at different intervals and thus reproduces those discrete positions digitally to produce digital waveform/signal. this process is called sampling. More the sampling, better the output signal.

The technical name for processing in this topic is termed as modulation and its been done with respect to three parameters of the signal/waveform, i.e. Amplitude, Frequency and Phase.

Thus there are basically three types of modulation.

Amplitude Modulation, Frequency Modulation and Phase Modulation.

DSP is widely used in 21st century. Its major applications include

1. Electronics and telecommunication sector.

2. Biomedical equipment.

3. Music Production/Sound engineering.

4. Military Machinery and advanced aviation.

5. Computer generated algorithms such as voice and face recognition's, A.I.

Audio world : MP3 compression audio signal processing, speech compression and transmission in digital mobile phones audio compression, hi-fi , speech processing, speech recognition.
Video : Digital image processing, video compression,

Others : Digital communications, radar, sonar, Specific examples are,
seismic data processing, analysis and control of industrial processes,
medical imaging such as CAT scans and MRI, computer graphics, image manipulation.

INTRODUCTION TO DIGITAL SIGNAL PROCESSORS

Digital signal processing

Have you used your cell phone lately? How about room correction in your hi-fi system? Well then, you have already witnessed Digital Signal Processing (DSP) in play. Digital signal processing is the process of digitizing real-world signals like temperature, audio, and pressure and then manipulating them mathematically using complex algorithms and conversion software. The information is then represented as discrete frequency, time or space so that it can be processed digitally. In the real world, analog-to-digital converters are used to convert analog signals like sound, pressure, temperature or light into 0’s and 1’s.

Components of Digital Signal Processor

A DSP contains four key components which include;

Compute machine: This performs the mathematical manipulations. It accesses a task or program from the Program memory and the data from Data memory.

Data Memory: This where the information to be processed is stored. This component works in tandem with the program memory.

Program memory: The program memory stores the programs or tasks to be used by the DSP in processing, compressing or manipulating data.

Input/Output: I/O can be used for numerous things depending on where the DSP is being used. It can be used for timers, serial ports, external ports and for connection to the outside world.

Analog-to-Digital Converter (ADC) and Digital-to-Analog Converter (DAC)

Electrical and electronic components are used in many facets of our daily lives. ADCs and DACs are critical components in any type of DSP in any fields. Both of them are necessary for in the conversion of real-world signals so as to allow digital equipment to pick up analog signals and process them.

Example of how DAC and ADC work

To understand hoe ADCs and DACs work, let us look at the example of an mp3 player. During recording, the analog audio passes through a microphone and amplifier. It then goes to the ADC where the collected analog signal is converted to a digital signal and then passed to the DSP which then processes the digital signal based on its internal algorithm before encoding it as an MP3. The DSP then finally saves the file to memory. A good example of a DAC is the digital ramp ADC

When playing, DSP decodes the MP3 file from the memory. It then passes the digital signal to the DAC, which converts thesignal to analog form. After amplification, the analog signal is output through a speaker. The DSP also handles user interfacing, level control, and equalization if need be.

Applications of Digital Signal Processors

There exist many variants of digital signal processors based on the functions they are intended to perform. What function a DSP can and cannot perform is determined by how it filters its inputs. DSPs can be used to perform the following functions

· Audio signal processing

· Digital image processing

· Audio and Video compression

· Speech processing and recognition

· Radar applications

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Source: Quora