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Signal Processing Research Papers

Signal Processing Research papers mainly deals with the design and implementation of systems for Signal processing and machine learning. Signal Processing actually deals with converting and transforming data. It always allows scientists to analyse, optimize, and correct signals.It plays a crucial role in a wide range of applications, spanning telecommunications, audio and speech processing, image and video analysis, biomedical engineering, radar and sonar systems, and more.

The technology we use in our daily life is depending on signal processing that includes computers, radios, video devices and cell phones and also smart connected devices, and more. In today's world, signal processing is serving as the heart of our modern world. It is the intersection of biotechnology. And also it comes under entertainment and social interactions.

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Speech and Audio Processing


Every telephone whether it is a smartphone or not, depends seriously on speech processing methodologies to develop voice communication among two or more people. Without signal processing, modern digital assistants, like Siri, Google Now, and Cortana, will not recognize the voice of the user.

Speech Recognition


Speech recognition is a major application of signal processing; it’s likely the easiest methodology for understanding. Signal processing uses information contained in signals to ease automatic speech recognition. It aids to extract information from the speech signals and then translates it into familiar words. Speech recognition technology is initially found in fighter aircraft, and it was implemented in "talk to text" applications on smartphones, therapeutic applications, language translation, learning, recognition programs for people with incapacities, and much more

Hearing Aids


The main parts of hearing aid technology have ‘4’ synchronized parts: receiver, processor, microphone, and power source. Signal processing is involving in picking up sounds in the environment and processing them to increase what the wearer hears. Immediately, sounds are changed from analog to digital and back to analog before the sound is projected into the ear.

Autonomous Driving


Self-driving vehicles depend on input from a multi-modular system of sensors, that includes ultrasound, radar, and cameras –and to stop crashing, they must change the developed information and filter it into data required to control action. Signal processing is essential to the technology. It helps to choose whether the car requires to stop or go and is part of the radar is utilized for decoding weather conditions like rain or fog.



The wearables market is one of the developing and successful fields. Technology and sensors made into clothing and fittings track levels, heart rates, GPS, sleep patterns, and much more. Signal processing helps in the wearable market field to collect information and translate them into useful data that is to be controlled in numerous ways likely reporting heart rate to your doctor or increasing your workout time to lose weight.

Data Science


As like signal processing, data science hits our daily lives in many ways. It is occupying a huge portion of our every walk of life. Whether it is using new data sources like emerging social media platforms, predicting changes in the stock market, or studying data to resolve medical issues ranging from diabetes to heart problems, signal processing makes it possible for evaluating data that advances our day-to-day life.

Communications Systems and Networks


Have you ever assumed about communicating with extraterrestrial beings? Signal processing is an essential part of searching for life outside the Earth. An important part of real communications across video, satellite, radio, and wireless systems, signal processing forms the processing and transmission of data more effectual.

The field of signal processing is on the brink of significant advancements and future expansions. One area of great promise is the integration of deep learning and neural networks into signal processing. Deep learning algorithms, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have demonstrated exceptional capabilities in tasks such as speech recognition, image and video analysis, and natural language processing. Their ability to automatically extract complex features from signals has the potential to revolutionize the way signals are processed and interpreted. By leveraging the power of deep learning, signal processing can achieve higher accuracy, faster processing speeds, and improved performance across various applications.

Advantages of signal processing

  • Digital data can be compressed very easily
  • It is fewer expensive
  • Can be stored easily on any magnetic media
  • It can be transmitted over long distances
  • Signals can be reproduced easily
  • Signal processing involves in transmitting information with less noise, alteration, and interference.

Disadvantages of Signal Processing

  • Sampling may cause loss of information
  • Processor speed is limited
  • Develop errors
  • It is highly complex.
  • A/D and D/A requires mixed-signal hardware
  • Higher bandwidth is required for the data communication process.