Explore MATLAB source code curated for "采样" with clean implementations, documentation, and examples.
This source code implements a Bayesian inference toolkit utilizing Markov Chain Monte Carlo (MCMC) methods, featuring MCMC sampling algorithms, Gaussian classification models based on MCMC, and detailed explanations of various sampling techniques. The package includes comprehensive documentation for easy implementation.
Record your own voice signal and sample the recorded signal; plot the time-domain waveform and spectrogram of the sampled voice signal; design a filter using the window function method and bilinear transform based on specified filter performance requirements, and plot the filter's frequency response; apply the designed filter to process the acquired signal, plot the filtered signal's time-domain waveform and spectrum, compare pre- and post-filtering signals, and analyze signal changes; playback the voice signal; finally, design a signal processing system interface with integrated functions.
The RRT (Rapidly-exploring Random Tree) algorithm is a sampling-based planning method that generates executable trajectories through kinematic and dynamic simulations. It avoids explicit space modeling by performing collision detection on sampled points in the state space, making it particularly suitable for solving path planning problems with motion dynamics constraints. Implementation typically involves key functions for random sampling, nearest neighbor search, and collision checking to efficiently explore the configuration space.
Implementing 3-level wavelet packet decomposition of speech signals and extracting decomposition coefficients using signal processing algorithms
Generate simulated BOC signals using MATLAB, perform signal sampling, and conduct spectral analysis using FFT with implementation guidelines.
Record your own voice signal, perform sampling, and visualize time-domain waveforms and spectrograms. Design filters using window function method and bilinear transform based on specified performance metrics, then analyze frequency response. Apply custom filters to voice signals, compare pre/post-filtering results, playback audio, and create a signal processing system GUI.
Implementation of PCM technology through three processes: sampling, quantization, and encoding. Sampling: Low-pass continuous signal sampling demonstrated using x=sin(200*t), m=x./(200*t), m=m.*m to illustrate sampling theorem with time/frequency domain plots; Band-pass continuous signal sampling using x=sin(20*t), m=x./t to demonstrate band-pass sampling theorem with corresponding plots. Quantization: Uniform quantization implemented for sinusoidal signals with 64 quantization levels; Non-uniform quantization applied to sinusoidal signals input to A-law PCM encoder, showing sampling sequence and output code sequence. Encoding: Implementation of A-law 13-segment approximation and international standard PCM logarithmic A-law quantization coding.
Implementing frame segmentation on sampled speech signals and storing the framed signals as a matrix for further analysis
This project involves recording a personal speech signal, performing sampling operations, and visualizing both time-domain waveforms and spectrograms. With specified filter performance metrics, we design filters using the window function method and bilinear transformation, then plot their frequency responses. The recorded signal is filtered using the custom-designed filter, and the filtered signal's time-domain waveform and spectrum are visualized. Finally, a comparative analysis between pre-filtered and post-filtered signals is conducted to examine signal changes.
1. Generate a continuous signal containing low, medium, and high frequency components, perform sampling and spectral analysis. Design three types of filters (high-pass, low-pass, and band-pass) to process the signal and observe the spectrum of filtered signals. 2. Acquire a noisy speech signal (either by recording with background noise or adding noise to clean speech), perform sampling and spectral analysis, then design an appropriate filter to eliminate noise based on spectral characteristics.