Explore MATLAB source code curated for "数字滤波器" with clean implementations, documentation, and examples.
A detailed simulation of digital filters, successfully demonstrating low-pass filtering with comprehensive implementation examples, algorithm explanations, and supporting visualizations.
Implementation of digital filter design through Butterworth filters in MATLAB with practical code examples and parameter configuration guidance.
Digital filters are computational systems that process digital signals to modify frequency components by altering their relative proportions or eliminating specific frequencies. Classic digital filters are categorized by their frequency response characteristics into low-pass, high-pass, band-pass, and band-stop types. The magnitude response illustrates amplitude attenuation across frequencies, while the phase response indicates time delays introduced by the filter. This course project introduces Chebyshev digital bandpass filter theory and implements a design using MATLAB's buttord and butter functions to analyze performance metrics through loss function and phase response visualizations.
Digital filters effectively process EEG signals by removing unwanted components and achieving noise reduction, typically implemented using FIR or IIR filter designs with frequency-selective techniques.
This program has been organized into a Word document and implemented using MATLAB programming language. The digital filter designed in this implementation is a bandpass filter, with all design parameters clearly specified in the documentation. The program has been verified as correct and executable, capable of generating graphical outputs. This implementation demonstrates proper filter design methodology using MATLAB's signal processing toolbox functions.
Project: Design and Application of Digital Filters Design Requirements: Using MATLAB software with composite signal separation as an example, this project simulates three key processes from the "Digital Signal Processing" course: spectral analysis, digital filter design, and signal filtering. Implementation involves creating composite signals, designing elliptic IIR filters (low-pass, band-pass types), and analyzing separation/resynthesis results through frequency domain and time domain observations.
1) Speech signal acquisition using audio recording functions 2) Spectral analysis through FFT implementation 3) Design of high-pass/low-pass digital filters with frequency response visualization 4) Signal filtering using convolution operations 5) Comparison of pre/post-filtering waveforms and spectra 6) Original signal audio playback 7) Time-stretching algorithms for fast/slow playback 8) Echo effect implementation using delay lines 9) Pitch shifting techniques for high/low voice effects 10) GUI system design with interactive controls
Introduction to digital filters including their definitions, classifications, and implementation approaches. Discusses design methodologies for IIR and FIR filters, along with practical implementation using MATLAB's DSP Blockset toolbox for digital filter design and simulation.
Design of FIR Digital Filters using Window Functions, Characteristics of Linear Phase Filters and Their Applications with MATLAB Implementation Examples
1. Design an FIR digital low-pass filter meeting analog filter specifications using triangular, Hamming, Blackman, and Kaiser windows; compare design performance across different windows. Requires plotting impulse responses, window functions, and magnitude/phase frequency response curves. 2. Apply designed filters to process given signal sets. 3. Design digital high-pass, band-pass, and band-stop filters with corresponding impulse responses and frequency response plots. Implementation involves using MATLAB's filter design functions (fir1, window functions) and frequency analysis tools (freqz, impz).