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Digital signal Processing techniques

Track :

Computer Science

Lessons no : 19

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What will you learn in this course?
  • Apply Fourier Transform techniques for frequency analysis in digital signal processing projects using MATLAB and Python
  • Design and implement digital filters to improve signal quality in audio, speech, and image processing applications
  • Utilize sampling theory to convert analog signals into digital form accurately for various signal processing tasks
  • Analyze biomedical signals to extract meaningful information for healthcare and medical diagnostics
  • Develop algorithms for noise reduction and signal enhancement in communication systems and multimedia applications
  • Model and simulate digital signal processing systems to optimize performance and efficiency in real-world scenarios
  • Apply spectral analysis methods to identify signal features and patterns in audio, speech, and image data
  • Implement real-time DSP algorithms for signal analysis, filtering, and system optimization in MATLAB and other tools

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Lessons | 19


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Related Courses

Digital signal Processing course, in this course offers a comprehensive journey into the core principles and practical applications of DSP. Designed for students and professionals, this course begins with foundational concepts, explaining how real-world analog signals are transformed into digital form for processing. You'll learn about key techniques, including Fourier Transform for frequency analysis, digital filtering for signal improvement, and sampling theory, which underpins the digital representation of signals. The course also dives into applications of DSP across various fields, such as audio and speech processing, image enhancement, biomedical signal analysis, and communications. Hands-on exercises with tools like MATLAB enable you to implement and test DSP algorithms, helping you build an intuitive understanding of digital filtering, spectral analysis, and system modeling. By the end of the course, you’ll have a solid grasp of DSP concepts and practical experience applying them, equipping you with skills to enhance data quality, design efficient digital systems, and solve complex signal processing challenges in real time.