Explore MATLAB source code curated for "FIR滤波器" with clean implementations, documentation, and examples.
Implementation of one-dimensional FIR filter design with arbitrary frequency response using second-order cone programming optimization, featuring mathematical formulation and practical coding approaches.
This FIR filter design implements audio noise reduction using signal processing techniques.
1. Understand FIR filter principles and implementation approaches including filter design specifications and parameter selection. 2. Master MATLAB-based FIR filter design techniques using built-in functions like fir1() and firls(). 3. Learn DSP programming methodologies for FIR filter implementation with optimized algorithms and real-time processing considerations.
Comprehensive FIR filter design techniques with MATLAB code examples covering windowing methods and frequency sampling approaches for digital signal processing applications.
Implementation of FIR filters in MATLAB including frequency response analysis, with verification using unit impulse function, periodic square wave sequence, and periodic triangular wave sequence to validate filter characteristics
FIR Filter Design: Customize filter type (low-pass, high-pass, or band-pass) and specifications (passband cutoff frequency, passband ripple, stopband cutoff frequency, stopband attenuation), select appropriate window functions based on specifications, design FIR filters using the window design method, and validate performance against predetermined specifications through MATLAB code implementation.
MATLAB program for digital down-sampling implementation featuring complete CIC, HB, and FIR filter code with algorithmic explanations
FIR filter design using Genetic Particle Swarm Optimization (GPSO) and Chaotic Particle Swarm Optimization (CPSO) with performance comparison. The filter parameters are adjustable, enabling optimal solution discovery through evolutionary computation techniques.
This course project involves acquiring speech signals via microphone and implementing filtering techniques for noise reduction. Developed on MATLAB 7.1 platform, the design process includes three key phases: capturing speech signals with spectral analysis, designing a Hamming window FIR filter, and applying the filter to enhance signal clarity. Post-filtering results demonstrate significant improvements in speech intelligibility, achieving the primary design objective through proper digital signal processing implementation.
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.