DUE DATE : 30 DECEMBER 2024
BERT3373 DIGITAL SIGNAL PROCESSING
TITLE : DIGITAL SIGNAL PROCESSING WINDOWING FILTERS
Windowing filters are a specific type of filter design technique used primarily in the
design of FIR filters (Finite Impulse Response filters). The windowing method helps
to shape the filter's frequency response by applying a window function to the ideal
impulse response.
When designing FIR filters, the ideal filter response (e.g., low-pass, high-pass) is
typically derived from an infinite-length impulse response, which is not practical to
implement. By using a window function, we can truncate this ideal response to a
finite length while controlling various aspects of the filter's performance.
Windowing Filters:
1. Rectangular Window
2. Hamming Window
3. Hann Window
4. Blackman-Harris Window
5. Kaiser Window
6. Bartlett Window
7. Gaussian Window
8. Welch Window
9. Flat Top Window
10. Nuttall Window
Each windowing function has different characteristics and is chosen based on the
trade-off between side-lobe attenuation, main-lobe width, and frequency resolution
required for a specific application.
Characteristics of Windowing Filters:
Side-Lobe Suppression: The ability of the window to reduce the levels of
spurious frequencies (side lobes) away from the main frequency peak.
Main-Lobe Width: The width of the central peak in the frequency response. A
narrower main lobe leads to better frequency resolution.
Frequency Resolution: The ability to distinguish between closely spaced
frequency components. A wider main lobe decreases frequency resolution.
Ringing Effect: The oscillations that appear in the frequency response due to
abrupt truncation, which windowing helps to reduce.
DUE DATE : 30 DECEMBER 2024
Uses of Windowing Filters:
FIR Filter Design: Windowing is commonly used to design FIR filters with
specific frequency response characteristics, such as low-pass, high-pass,
band-pass, and band-stop filters.
Spectral Analysis: In applications like speech and audio processing,
windowing is applied to signals before performing a Fourier transform to
minimize leakage and improve frequency resolution.
Signal Processing: Windowing is used to smooth or shape signals in time or
frequency domains, especially when analyzing signals with varying
characteristics.
OBJECTIVES :
Able to;
1. Demonstrate the characteristics and differences of different filters
2. Organize technical content
3. Work in a team (group size 2 min. – 4 max.)in presenting meaningful and
interesting content
ASSIGNMENT INSTRUCTIONS :
1. Demonstrate your understanding of different windowing filters (at least two).
2. Demonstrate how to design the filters (that you have chosen).
3. Demonstrate one application of each filter.
4. Present your work in an interesting video and report format
5. Your video should contain :
a. Explanation of the chosen windowing filters.
b. Explanation real application of the filters.
REPORT SUBMISSION :
1. Upload your video in Youtube.
2. Submit your report in Ulearn, with the link to your Youtube video
(include your team members and section in your submission)
3. The duration of your video should be between 10 min. -15 minutes max..
IMPORTANT REMINDER :
SUSPICION OF PLAGIARISM WILL BE SEVERELY PENALIZED.
