Welcome to the Digital-Signal-Processing repository! This repository contains my work for the DSP course, where I implemented solutions for assignments related to signal processing. The focus is on understanding and applying fundamental concepts to enhance, filter, and manipulate signals.
In this assignment, I utilized the Fast Fourier Transform (FFT) to analyze the frequency components of an audio signal. Key tasks included:
- Loading an audio file and visualizing it in both time and frequency domains.
- Identifying key features, such as fundamental frequencies and harmonics.
- Improving the quality of the audio signal through frequency domain manipulation, focusing on enhancing the clarity of the voice.
The second assignment focused on designing and implementing Finite Impulse Response (FIR) filters to process ECG signals. Highlights included:
- Task 1: Designing combined high-pass and band-stop FIR filters using the sinc function and applying a Blackman window.
- Task 2: Applying the designed FIR filter to noisy ECG signals and comparing the results with the clean ECG signal.
- Task 3: Using adaptive LMS filtering for noise removal and comparing the FIR and LMS-filtered signals.
- Task 4: Implementing heartbeat detection using matched filtering and calculating the momentary heart rate (BPM).
Each task included detailed visualization and analysis in both time and frequency domains.
Instead of Assignment 3, I completed a Moodle Quiz focused on Infinite Impulse Response (IIR) filters. The quiz covered:
- Theoretical aspects of IIR filters, including stability, poles, and zeros.
- Comparison between IIR and FIR filters.
- Real-time applications of IIR filters and their advantages in specific scenarios.
- Assignment_1_FFT/: Contains the code, plots, and audio files used for the FFT analysis, as well as the report summarizing the results.
- Assignment_2_FIR/: Includes FIR filter design, implementation scripts, ECG data files, and resulting plots for all tasks.
- README.md: This document, providing an overview of the repository.
- Python: Main programming language for all assignments.
- NumPy: Used for numerical computations, including FFT and FIR filter implementation.
- SciPy: Employed for advanced signal processing tasks and filter design.
- Matplotlib: Used for visualizing signals in both time and frequency domains.
To run the code for any of the assignments, follow these steps:
- Clone the repository:
git clone https://github.com/basav-sketch/Digital-Signal-Processing.git
- Navigate to the desired assignment folder.
- Run the Python scripts using Python 3.
Ensure you have the required Python libraries installed:
pip install numpy scipy matplotlib- Visualized the time and frequency domain of an audio signal.
- Enhanced the clarity of the signal by removing unwanted frequency components.
- Demonstrated the significance of FFT in audio signal processing.
- Designed and implemented FIR filters for ECG signal processing.
- Demonstrated the effectiveness of FIR filters in reducing noise in ECG data.
- Compared FIR and LMS filtering techniques, highlighting their strengths and trade-offs.
- Implemented a matched filter for heartbeat detection and calculated momentary heart rates (BPM).
- Extend FIR and LMS filtering techniques to other biomedical signals.
- Explore real-time DSP applications using adaptive filters.
- Experiment with machine learning for automatic filter design and noise classification.
This project is licensed under the MIT License - see the LICENSE file for details.
For any questions or discussions, feel free to reach out via GitHub or email.
Thank you for exploring my Digital Signal Processing assignments!