窗函数 Resources

Showing items tagged with "窗函数"

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.

MATLAB 307 views Tagged
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Time-domain representation using Short-Time Fourier Transform (STFT) with 1000 sampling points, applying windowing functions, and visualizing original signals through 3D spectrograms and contour plots.

MATLAB 254 views Tagged
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Welch Method Power Spectral Density (PSD) utilizes a modified averaged periodogram approach to estimate the power spectral density of random signals. Key algorithmic implementations include signal segmentation with overlapping windows, window function application, and FFT computation to determine single-signal power spectral density. Common MATLAB implementations involve using the pwelch() function with parameters for segment length, overlap percentage, and window type.

MATLAB 366 views Tagged
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MFCC, or Mel-Frequency Cepstral Coefficients, represent one of the fundamental features in speech signal processing that effectively models human auditory perception. The computational pipeline involves preprocessing, windowing, Fourier transformation, power spectrum calculation, natural logarithm application, and discrete cosine transform (DCT). The MATLAB implementation leverages a speech processing toolbox available for online download, with key functions including frame segmentation, FFT operations, and Mel-filterbank integration.

MATLAB 303 views Tagged
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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).

MATLAB 236 views Tagged
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Speech signals are time-varying in nature, with individual parameter variations occurring more gradually than the signal itself. Consequently, measuring these parameters requires a significantly lower sampling frequency compared to the signal's original sampling rate. Through window function weighting, the signal is segmented in the time domain into local signal sequences for measurement. Proper short-time analysis requires defining two key dimensions: window length (duration of the weighted signal segment) and measurement interval (frame rate, representing the spacing between consecutive windows). Core short-time analysis operations include short-time energy (reflecting amplitude variations), short-time autocorrelation function (detecting periodicity), and short-time zero-crossing rate.

MATLAB 242 views Tagged
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