---
name: signal-processing-f6bf255f7098
description: Performs signal processing tasks including spectral analysis (FFT), digital filtering, time-frequency decomposition, noise reduction, and modulation/demodulation; trigger when users discuss waveforms, frequency spectra, filters, or time series in engineering contexts.
---

## 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

1. **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).
2. **Preprocessing** - Remove DC offset (mean subtraction). Apply windowing (Hann, Hamming, Blackman) to reduce spectral leakage. Handle missing data or outliers. Normalize amplitude if needed.
3. **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.
4. **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).
5. **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).
6. **Denoising** - Estimate noise characteristics (white, colored, impulsive). Apply appropriate method: spectral subtraction, Wiener filter, wavelet thresholding (soft/hard), or adaptive filtering (LMS, RLS).
7. **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


---
Source attribution: beita6969
Original project: https://github.com/beita6969/ScienceClaw/blob/e925f732c7ca435f557e88075bc47a58f1e984f1/skills/signal-processing/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2025 Peter Steinberger

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: beita6969
Original project: https://github.com/beita6969/ScienceClaw/blob/e925f732c7ca435f557e88075bc47a58f1e984f1/skills/signal-processing/SKILL.md
Attribution basis: Source repository record

Fields of work: Data & Analytics, Development & Engineering, Education & Research, Product Management
Roles: Backend Developer, Data Scientist, ML / AI Engineer, Product Manager, Researcher
Specialties: API, Backend
Tools: Librosa, Matlab, Pywavelets, Scipy, Gnu Radio
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 3ce286d148c57dab84d98df3cb08f0ee95419ce2f0eb02e420cd72e2ef85e820