Real Time Implementation of Multi-Level Perfect Signal Reconstruction Filter Bank

Muhammad Amir Shaﬁq, Saqib Ejaz

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Discrete Wavelet Transform (DWT) is an efﬁcient tool for signal and image processing applications which has been utilized for perfect signal reconstruction. In this paper, twenty seven optimum combinations of three different wavelet ﬁlter types, three different ﬁlter reconstruction levels and three different kinds of signal for multi-level perfect reconstruction ﬁlter bank were implemented in MATLAB/Simulink. All the ﬁlters for different wavelet types were designed using Filter Design Analysis (FDA) and Wavelet toolbox. Signal to Noise Ratio (SNR) was calculated for each combination. Combination with best SNR was then implemented on TMS320C6713 DSP kit. Real time testing of perfect reconstruction on DSP kit was then carried out by two different methods. Experimental results accede with theory and simulations.

Summary

This paper evaluates and implements multi-level perfect reconstruction filter banks using the Discrete Wavelet Transform (DWT). It compares 27 combinations of wavelet filter types, reconstruction levels, and signal types via SNR in MATLAB/Simulink, then deploys the best-performing design to a TMS320C6713 DSP for real-time validation.

Key Takeaways

Compare 27 DWT-based filter-bank configurations and pick the best by SNR.
Design and tune wavelet filters using MATLAB's Filter Design Analysis (FDA) and Wavelet Toolbox.
Implement multilevel perfect reconstruction filter banks in MATLAB/Simulink for hardware deployment.
Deploy and validate the selected filter-bank on a TMS320C6713 DSP kit using two real-time test methods.

Who Should Read This

DSP engineers or researchers with experience in wavelet/filter-bank design who want practical guidance on MATLAB/Simulink development and real-time deployment to TMS320C6713-class hardware.

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Topics

Wavelets MATLAB/Simulink Real-Time DSP Filter Design