DSP projects using Python give you an insight into the different data sets for Digital Signal processing and doing signal processing projects with the help of Python.  Digital Signal processing is the method in which the obtained input signals are operated using existing algorithms and datasets. Real-time signal processing rather than using datasets is also

possible with Digital Signal processing.

A signal can be of multiple forms. Everything around us is a signal that can be interpreted and converted into its exact digital counterparts. 

  • The signals include sound, video, medical images, natural signals like seismic data etc
    • Under sound signals there are multiple variants. To be technical let us consider radar and Sonar.
    • Sonar signals produce the data about variation in acoustic pressure with respect to time in three spatial dimensions
    • Radar also gives the same data especially in case of electromagnetic waves
  • For a signal in the form of an image multiple information (to spatial dimensions) can be obtained from it which include
    • Brightness
    • Contrast
    • Fine details

In the upcoming sections, let us have a brief Idea on signal processing projects especially using Python. 

Digital Signal Processing using Python

Python is easy to handle software for anyone attempting to design Signal processing projects (DSP Projects using Python). It provides for the following 

  • Open source packages
  • Filtering functions
  • Libraries
  • Tools for designing filters
  • B spline interpolation algorithms (for both 1 and 2 dimensional information)

Experts with us have been using Python for quite a long period of time, not only for Digital Signal processing projects but also for many other advanced topics. So our experts can give you complete guidance regarding the usage of python toolboxes. 

We provide exceptional research support online to students and research scholars from any part of the world. We encourage doing research on novel ideas. So we assist you in any topic of your interest. First, in order to come up with your own idea, you should be aware of the trending research topics in the field of digital signal processing


By referring to world-class sources and having talks with scientists and researchers in the international forums, our experts have come up with a list of research topics that are trending in digital signal processing. We have listed them below.

  • Channel coding
  • OFDM and MIMO systems
  • Processing audio signals (speech)
  • Processing signals which are function of space and time.
  • Processing and coding signals
  • Detecting and estimating signals
  • Coding and processing of image signals (including videos)

Research in digital signal processing using python has developed exponentially over the past few decades. This can be mainly attributed to the demands arising out of the everyday applications related to the processing of digital signals. 

One such demand over signal processing research is the development of real-time signal processing. Now let us have some idea on real-time digital signal processing areas. 

Research DSP Projects Using Python Programming


Processing of signals in Real-Time requires some advancement both in the hardware and software part. So research to improve upon the existing real-time signal processing techniques is trending these days. Now let us see some of the real-time digital signal processing areas below.

  • Detection of people in roads (pedestrian, bicyclist etc)
  • UAVs or unmanned aerial vehicles functioning
  • VR or virtual reality applications
  • Processing images using computer vision technology
  • Implantation of medical devices
  • Communication system applications (especially optical)
  • Recognition of words (and speech)
  • Monitoring the functions of brain
  • Internet of things applications
  • Radar communication (including cellular)

The above list of research areas in real-time signal processing has been developed over the recent past. So it should be understood that Digital Signal processing research is an ever-widening field of study.

We provide research support on any topic that belongs to the above DSP research areas. Also, our researchers are developing some advanced novel methods in digital processing, for which our experts are giving complete support. Now let us have some idea on the research areas which are specific to a particular field of application, starting with the medical field.

Digital Signal Processing in Medical Field

Digital Signal processing is almost the backbone of the medical field. Detection of diseases, diagnosis, and treatment everything depends on processing the digital signals. The following are the important applications of the DSP system in the medical field. 

  • Measurement of heart rate
  • Predicting lung ausculations
  • Characterizing intensity
  • Monitoring blood pressure (systolic)
  • Detecting epileptic seizure
  • Assessing the quality of EEG signals

The above research areas in the medical field are related to improving the existing digital signal processing system used in it. We have designed many signal processing systems for medical applications.

So we can support you in designing an advanced system for processing medical signals. Our research experts have gained more knowledge related to the techniques used in complex signal processing system design. Now let us see about digital signal processing systems in the field of communication.

Digital Signal Processing in Communication Systems

Communication is one of the most popular applications of signal processing systems. Now let us see the important areas of research in the communication field that require the use of digital signal processing. 

  • Modulating and demodulating signal
  • Removal of noise from the signal
  • Segmentation of wave form
  • Encoding information (and also decoding)
  • Detecting faults
  • Spectrum analyser
  • MU systems(using many antenna)

The above areas of research are gaining importance day by day due to the fact that complications and diseases requiring in-depth study are developing everywhere. So for doing advanced studies in the medical field, we require an advanced mechanism for processing medical signals. For this purpose, Python is of great use. What are the specialties of Python?

  • It is a user-friendly platform
  • High level and advanced programming functions
  • There is better community support
  • It can be used across multiple platforms
  • Easy to interpret programming codes
  • Advanced libraries (of high quality)
  • Specific and variety of computational support

Therefore Python is the best tool for answering any complicated research questions. Complex investigations on research issues can be easily handled using Python. 

Our experts are very well qualified in using Python for different research projects. They have handled the problems arising out of it and had devised the best possible solutions. You can readily connect with us at any time and get expert advice from our technical team for DSP projects using Python. Now let see about the python libraries for DSP projects.

Digital Signal Processing Python Library

There are advanced libraries present in Python. You should have already used these libraries in any of your projects. Our experts have used Python for almost all the digital signals processing projects that we rendered guidance. Now let us see a description of the different python libraries that are suitable for use in Digital Signal processing projects.

  • SciPy
    • Numerical algorithm
    • Toolboxes specific to domains
    • Tool boxes for performing signal optimization, processing, statistical operations etc
  • PyAudio
    • Binds portaudio
    • Provides for audio input and output library (cross platform)
    • Play audio (and also recording)
  • NumPy
    • Scientific computations
    • Contains multi dimensional array objects
    • Functions for broadcasting
    • Fotran and C/C integration
    • Mathematical operations like transforms (Fourier), algebra etc
  • Matplotlib
    • Two dimensional plotting library
    • Publishes high quality figures
    • Supports various formats
    • Interactive actions
  • pyrtlsdr
    • Simple interface
    • RTL – SDR projects supported
    • Obtaining and designing radio receivers using Realtek RTL2832U(from USB DVB – T dongles)
    • It provides asynchronous read support
    • It gives many pythonic API

These datasets are extremely useful in DSP projects. There are several theories and applications in digital signal processing by these python libraries. You can gain expertise in digital signal processing by pointing to the newer areas that have the potential to register tremendous growth by its use. 

For this purpose, we provide you with circumstantial evidence with proofs of performance evaluation of the DSP projects using Python that we delivered. This will eventually lead you to a whole new idea on how Python is exclusively used for designing digital signal processing projects.

  You can determine the quality of our research guidance facility from the achievement records of our customers across the world. Now let us look into one of the important parts of using Python that is datasets. Let us start by seeing datasets for processing EEG signals.


The following data sets for EEG signals have gained more attraction due to their active use by researchers around the world. 

  • BCI competition (picking abnormal EEG signals)
  • Enterface ’06 and DEAP (for recognising emotions)
  • Psyhophysics (for identifying visual attention)
  • EID – S and EID – M (eye movement identification)
  • Epilepsy (brain disorder prediction)
  • EEG – VR, EEG – VV and EEG – IO (detection of eye blinking)

There are certain concerns with these datasets that are flagged by researchers in recent times. This can be rectified with in-depth research and analytical study. 

Our research experts are currently working on developing ready-to-use medical imaging devices with different EEG signal data sets. We are examining the possibilities for further advancements in the field of medical imaging techniques using Python for DSP projects. Now let us look into some of the important datasets for ECG processing.


The following datasets are very much useful for ECG processing. It allows the researchers to make accurate predictions and make both qualitative and quantitative analyses.

  • Arrhythmia data set (distinguish and classify cardiac arrhythmia)
  • Wearable ambulatory ECG and EEG data set
    •  Records easy signals during movements
    • System for signal acquisition – Biopac MP36
    • It comes with the following specifications.
      • Number of subjects studied – 10
      • Movements involved – waist twist, sitting and standing, up and down movement of both arms.
  • D1NAMO
    • Conditions of real life circumstances are recorded
    • Zephyr BioHarness
    • It involves the following
      • ECG signals
      • Measuring glucose
      • Breathing signals (accelerometer)
      • Food pictures (annotated)
  • ECG signals
    • PhysioNet service data are used for research
    • MIT – BIH Arrhythmia database
    • Dataset specifications
      • Patients – 29
      • Classes of signals – 17 (normal sinus and pacemaker rhythms)
      • Types of dysfunction (cardiac) – 15 (with 10 signal fragments)

Recently we made medical imaging algorithms with the use of the above datasets. The choice of data set varies with the specific applications.

Our experts have got huge knowledge with respect to these datasets. Let yourself take advantage of the expert support in research guidance from our world-class certified engineers. Now let us see about other advanced datasets.


The study on the following datasets can give us an insight into doing future research on various methods related to image processing techniques. 

  • Digital forensics database (files of audio and video forensics)
  • QSBD or Quantum Signal Biometrics Database
  • Still images
  • Videos
  • High quality data set of 300 subjects
  • Greatly useful in developing biometrics recognition (face, speech etc.)
  • CASIA Face Image Database (version 5.0)
  • It contains the following
  • 2500 images of 500 subjects
  • File specifications – 16 bit color (BMP files)
  • Resolution – 640*480
  • Variations based on pose, illumination, distance of imaging, expressions etc
  • Intel Open Wi-Fi RTT dataset
  • Measurements made – 30,000 (real life raw channel recordings)
  • Extremely useful in determining the following
  • Ranging
  • Position
  • Time of arrival
  • Navigation research
  • Indoor Wi-Fi

Usually, in the medical field, there are multiple ways in which signal processing techniques can be used. With greater achievements by researchers in signal processing, there are also several other fields of daily importance in which digital signal processing has gained immense significance. If you are struggling to develop code using python, reach our experts for project guidance. We help you to implement DSP projects using Python Programming as first choice to simulate your research work. Connect with us to get more details on our projects. 

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