beita6969 When to Trigger
Activate this skill when the user mentions:
- FFT, DFT, spectral analysis, power spectral density
- Digital filters (FIR, IIR), Butterworth, Chebyshev
- Time-frequency analysis, STFT, wavelets, spectrograms
- Signal denoising, SNR, noise floor
- Sampling, Nyquist theorem, aliasing, ADC/DAC
- Modulation (AM, FM, QAM), demodulation, baseband
- Convolution, correlation, matched filtering
Step-by-Step Methodology
- Signal characterization - Identify signal type (continuous/discrete, deterministic/stochastic, stationary/non-stationary). Determine sampling rate, duration, and bit depth. Check Nyquist criterion (fs > 2*fmax).
- Preprocessing - Remove DC offset (mean subtraction). Apply windowing (Hann, Hamming, Blackman) to reduce spectral leakage. Handle missing data or outliers. Normalize amplitude if needed.
- Spectral analysis - Compute FFT with appropriate zero-padding for frequency resolution. Estimate power spectral density (Welch's method for noise reduction, periodogram for snapshot). Identify dominant frequency components and harmonics.
- Filtering - Design filter based on requirements: passband/stopband frequencies, ripple, attenuation. Choose type: FIR (linear phase, higher order) or IIR (lower order, nonlinear phase). Implement using appropriate method (Parks-McClellan for FIR, bilinear transform for IIR).
- Time-frequency analysis - For non-stationary signals: compute STFT (spectrogram) with appropriate window size trade-off. Apply wavelet transform (CWT for analysis, DWT for decomposition/compression). Select mother wavelet (Morlet for frequency, Daubechies for transients).
- Denoising - Estimate noise characteristics (white, colored, impulsive). Apply appropriate method: spectral subtraction, Wiener filter, wavelet thresholding (soft/hard), or adaptive filtering (LMS, RLS).
- Validation - Verify filter response meets specifications (frequency response, phase response, group delay). Check for artifacts (ringing, Gibbs phenomenon). Compute output SNR improvement.
Key Databases and Tools
- SciPy signal - Python signal processing functions
- MATLAB Signal Processing Toolbox - Comprehensive DSP tools
- GNU Radio - Software-defined radio framework
- Librosa - Audio signal processing
- PyWavelets - Wavelet transform library
Output Format
- Frequency spectra with labeled axes (Hz or normalized frequency, dB or linear magnitude).
- Filter specifications: type, order, cutoff frequencies, passband ripple, stopband attenuation.
- Time-frequency plots (spectrograms) with time, frequency, and magnitude axes.
- SNR values in dB before and after processing.
- Transfer function coefficients (numerator b, denominator a for IIR; taps for FIR).
Quality Checklist
- Sampling rate and Nyquist criterion verified
- Windowing function specified and justified
- FFT length and frequency resolution stated
- Filter order and stability verified (all poles inside unit circle for IIR)
- Phase response considered (linear phase requirement?)
- Group delay acceptable for application
- SNR improvement quantified
- Edge effects and transient responses handled