IIR and FIR Filter Design | For this project you will design a compare several IIR and FIR filters. [docx] |
Upsampler Filter Design | The project overall deals with designing an interpolation filter for upsampling. The upsampling requires a lowpass interpolating filter. In this project we are not concerned with the issue of dynamic range (which relates to the number of bits used internally to implement the interpolating filter), i.e., you only need to consider the specifications for the passband and stopband edge frequencies, the maximum passband ripple, and the minimum stopband attenuation. This project is an excellent introduction to the concepts required in Filter Design – Philips Upsampler. [docx] |
Filter Design - Philips Upsampler | In this project the filter specifications are severe, resulting in very high order filters. Often the resulting systems are not be stable, often caused by coefficient quantization—even with floating point precision. What you need to do is to group the poles and zeros for the desired filter in pairs to create smaller stable second order filters of the form N(z)/D(z) where N(z) and D(z) are at most second order polynomials in z. A cascade of such filters produces the desired system. You will compare this design with a multi-stage interpolator and experiment with polyphase filters. [Files] [docx] |
Testing a “Folk Theorem” | A common “folk theorem” states that the ear is insensitive to phase, i.e. that for audio, phase distortion is inaudible. If that is correct, then processing audio with an all-pass filter should not result in perceived distortion. This project tests this conjecture. [Files] [docx] |
Enhancing Speech by Removing Noise | In this project we consider IIR and FIR filter design in the context of enhancing speech corrupted by additive noise. [Files] [docx] |
Hardware Implementation Considerations for Removing Noise | In this project, you will design filters for a “hardware” implementation of the Enhancing Speech by Removing Noise project. Since the de-noising filter does a good job of keeping the resulting signal band-limited to 4 kHz, it is prudent to add a compressor-by-4 (which for no aliasing requires its input to be band-limited to 5512.5 Hz) as a final stage in the implementation of our system. [Files] [docx] |
Matlab Ripple | MATLAB’s definition of ‘ripple’ differs between the IIR and FIR filter design functions. This project sorts through these differences. [docx] |
Designing and Using IIR Filters | This project is intended to give you practice using MATLAB to design and use IIR filters. Particular emphasis will be given to the use of IIR filters in decimation, and to the sensitivity of filter characteristics to quantization of the coefficients. [Files] [docx] |
Designing and Using FIR Filters | This project is intended to give you practice using MATLAB to design and use FIR filters. You will work on designing approximations to ideal low pass filters, with comparisons to IIR designs, and then examine signal interpolation using polyphase. [Files] [docx] |
Filter Design for Sample Rate Conversion | This project requires you to implement several discrete-time systems for sampling rate conversion. Specifically, this part focuses on experimentation with various filter classes within the context of antialiasing filter design. [docx] |
Spectral Analysis and Sample Rate Selection | Our goal is to design a very simple audio compression system. Audio that is originally sampled at 44.1 kHz will be processed to reduce the sampling rate to R Hz. Then, the samples at the lower sampling rate will be quantized to B bits per sample for a compressed bit rate of RB bits per second. [Files] [docx] |
Home
Oppenheim and Schafer, Discrete-Time Signal Processing ISBN 0-13-198842-5.
Prentice Hall, Upper Saddle River, NJ 07458.
© 2010 Pearson Education, Inc.