Near-Perfect Reconstruction Polyphase Filter Bank Implementation Example

This implementation showcases a near-perfect reconstruction polyphase filter bank system comprising both analysis and synthesis filter components. The system allows dynamic adjustment of various prototype filter coefficients through parameter configuration, enabling flexible filter design optimization. Reconstruction error simulation is implemented using difference calculation between original and reconstructed signals, typically measured through metrics like Mean Squared Error (MSE) or Signal-to-Reconstruction-Error Ratio (SRER). The workflow includes: 1) Designing prototype filters using windowing methods (e.g., Hamming, Kaiser) or equiripple techniques 2) Implementing polyphase decomposition for computational efficiency 3) Applying analysis filters for subband decomposition 4) Processing subband signals 5) Using synthesis filters for signal reconstruction. Key functions involved may include filter design routines (fir1, firpm), polyphase decomposition (polyphase), and error analysis algorithms. This comprehensive approach facilitates deeper understanding and performance optimization of polyphase filter bank systems for applications like spectral analysis and multirate signal processing.