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|Speech and Audio Processing
Image Processing and Analysis
Communications Systems and Networks
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.
Have you shared "what’s on your mind” with Facebook friends or searched the Internet today? These are just two of the myriad ways we use machine learning every day. Machine learning brings together computer science and statistics to harness predictive power, and provides the technology behind other applications, including detecting credit card fraud, medical diagnostics, stock market analysis and speech recognition.
Recently, machine learning techniques have been applied to aspects of signal processing, blurring the lines between the sciences, and causing many shared applications between the two.
For more information on machine learning and its applications, visit:
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.
For more information on speech and audio processing and their applications, visit:
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.
For more information on speech recognition and its applications, visit:
Can you hear us now? The core of hearing aid technology is four synchronized parts: microphone, processor, receiver and power source. Signal processing is involved in picking up sounds in the environment, and processing them to enhance and amplify what the wearer hears. Without delay, sounds are converted from analog to digital and back to analog before sound is projected into the ear.
While the fundamental components of the technology will remain the same, hearing aids are becoming increasingly more advanced – reducing noise and feedback from the surrounding environment to help people hear crisp, clear sounds. Signal processing also helps reduce sudden loud noises, such as horns, and even allows hearing aids to connect wirelessly with a cell phone or TV.
For more information on hearing aids and their applications, visit:
Once the stuff of science fiction, autonomous cars are now reality. To work properly, these self-driving vehicles rely on input from a multi-modular system of sensors, including ultrasound, radar and cameras –and to prevent crashing, they must convert the acquired information and filter it into data needed to control action. Signal processing is integral to the technology. It helps decide whether the car needs to stop or go and is part of the radar used to decipher weather conditions like rain or fog.
For more information on autonomous driving and its applications, visit:
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:
Here are number of pointers to applications and courses showcasing image processing across various domains:
The wearables market is emerging and already thriving. Technology and sensors built into clothing and accessories track fitness levels, heart rates, physical location (GPS), sleep patterns and more. Signal processing helps collect this information and translate it into useful data to be leveraged in myriad ways – such as reporting heart rate to your doctor or upping your workout routine to lose weight.
For more information on wearables and their applications, visit:
Every time you search on the Internet or post on Twitter, you’re adding data to what is popularly called “Big Data” sets. Companies utilize this data to extract information, learn about behaviors and create solutions to make our lives more efficient. Neuroscience and medicine relies on machine learning tools mixing imaging date with medical records and genomic to better understand and phenotype degenerative processes and diseases, predict responses to treatment, and cluster patients into subgroups, for example.
“Big Data” is a rapidly emerging field, but we’re faced with the challenge of studying large sets of technically difficult data. What’s the key component for analyzing data and solving complex problems? Signal processing.
Like signal processing, data science touches our daily lives in more ways than we think. Whether it’s using new data sources like emerging social media platforms, predicting changes in the stock market, or studying data to solve medical problems ranging from diabetes to heart problems, signal processing makes it possible to analyze data that enriches our lives every day.
For more information on data science and its applications, visit:
Have you ever thought about communicating with extraterrestrial beings? Signal processing is integral in searching for life beyond Earth. An important aspect to effective communications across satellite, video, radio and wireless systems, signal processing makes the processing and transmission of data more efficient.
When you’re on the go and need web access or using GPS to find your way, signal processing is the behind-the-scenes technology transforming and analyzing signals to help us communicate and learn from the technology we use on a daily basis – including cell phones, WiFi, TVs, GPS devices, radar, sonar, radio, and cloud and mobile computing.
For more information on communication systems and networks: