What do you mean by signal processing?
Signal processing involves converting or transforming data in a way that allows us to see things in it that are not possible via direct observation. Signal processing allows engineers and scientists to analyze, optimize, and correct signals, including scientific data, audio streams, images, and video.
The technology we use, and even rely on, in our everyday lives - computers
, radios, video, cell phones is enabled by signal processing, a branch of electrical engineering
that models and analyzes data representations of physical events. Signal processing is at the heart of our modern world, powering today's entertainment and tomorrow's technology. It's at the intersection of biotechnology and social interactions. It enhances our ability to communicate and share information. Signal processing is the science behind our digital lives.
Speech and Audio Processing
Every telephone, smart or not, relies heavily on speech processing techniques to make voice communication between two (or more) people possible. From analog-to-digital conversion to speech enhancement (filtering, echo-, noise-, and automatic gain control) to speech encoding on recording side to speech decoding to speech enhancement (typically filtering and gain control) to digital-to-analog conversion on the playback side. Signal processing is the tool of choice every step of the way. Without signal processing, modern digital assistants, such as Siri, Google Now, and Cortana, would not be able to recognize a user's voice.
Audio compression techniques, such as MP3 and AAC, have revolutionized the way we listen to music. We can now hold the world's music musical catalog in the palm of our hands and enjoy listening to music on-the-go, even completely untethered via Bluetooth. Again, signal processing made this happen.
Speech recognition is a vital application of signal processing; it's also likely the easiest to understand. Signal processing manipulates information content in signals to facilitate automatic speech recognition (ASR). It helps extract information from the speech signals and then translates it into recognizable words. Speech recognition technology is found in fighter aircraft, talk to text- applications on smart phones, therapeutic applications, language translation and learning, and recognition programs for people with disabilities.
Image Processing and Analysis
Seeing is believing. The omnipresence of digital cameras and screens in our daily lives, such as in our smartphones, cars, drones, surveillance systems, airplanes, hospitals, and our living room, translates our ever growing need to see, share and interact with our visual environment, with increasing levels of detail. In medicine almost all diagnosis nowadays involve some sort of imaging. However, this rapidly emerging part of the iceberg hides an important number of lesser-known, but highly essential applications, notably in the cultural, military, health and scientific research domains.
Signal processing is key to a wide range of applications, from acquisition to display:
- Digital restoration of images and videos
- Compressed sensing acquisition (e.g. single-pixel cameras)
- Image enhancement during acquisition (within camera chips)
- Image reconstruction from non-image sensors
- Image quality assessment
- Compression and transmission across networks and devices
- Image to display technologies (Color mapping, 3D to 2D projection)
- Detection, counting and tracking of features in images and videos
- Computer-aided diagnosis in medical imaging (e.g. assistance for mammogram reading)
- Automated analysis of biological images (e.g. cell tracking)
Where is signal processing used?
Digital signal processing is used everywhere. DSP is used primarily in areas of audio signal, speech processing, RADAR, seismology, audio, SONAR, voice recognition, and some financial signals.
What are the steps in digital signal processing?
- Sampling. The audio data we wish to treat will generally be present in the form of electric oscillations.
- Power and Energy.
- Fourier Transform.
- Convolution, Filtering and Linear Systems.