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signal-processing

Filter noisy time-series data and extract signal characteristics using scipy.signal.

33|6|Updated Mar 17, 2026
Search Tags:#filter design#spectral analysis#peak detection#digital signal processing#power spectral density#adaptive filtering#scipy.signal
One-click install
npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill signal-processing-xjtulyc
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: signal-processing
Source: https://github.com/xjtulyc/awesome-rosetta-skills/tree/main/skills/06-engineering/signal-processing
Command: npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill signal-processing-xjtulyc

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, scipy, matplotlib, pandas.

What problem does it solve?

It solves the challenge of turning noisy time-series data into reliable filtered signals and meaningful measurements like spectra, envelopes, and event peaks.

Core Features & Use Cases

  • Filter design and application: Build and apply bandpass/lowpass/notch filters (IIR/FIR options) for tasks such as biomedical artifact removal and vibration denoising.
  • Spectral analysis: Compute spectrograms (STFT) and estimate PSD via Welch’s method to reveal dominant frequencies and power distribution.
  • Detection and analysis: Detect peaks, compute cross-correlation, run matched filtering for template-based detection, and use LMS adaptive filtering for noise cancellation scenarios.

Quick Start

Use the signal-processing skill to filter a sampled signal, compute its PSD with Welch’s method, and return the frequency bins and power values.

Frequently Asked Questions about signal-processing

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I design and apply a bandpass filter to noisy time-series data using scipy.signal?▼

To perform spectral analysis and estimate power spectral density, this skill computes spectrograms using STFT and applies Welch's method via scipy.signal to reveal dominant frequencies and power distribution in time-series data.

What's the best way to detect peaks in a noisy signal with numpy and scipy?▼

The best way to detect peaks in a noisy signal is using this skill's detection algorithms, which leverage numpy and scipy to extract event peaks from filtered time-series data for vibration and biomedical analysis.

Can I use LMS adaptive filtering for noise cancellation in biomedical signal processing?▼

Yes, this skill supports LMS adaptive filtering for noise cancellation in biomedical signal processing, implementing adaptive algorithms to suppress unwanted noise components from sampled time-series signals.

Does this signal processing skill work with pandas dataframes and matplotlib for visualization?▼

Yes, this skill works with pandas-compatible data handling for input management and matplotlib for visualization, ensuring filtered signals, spectrograms, and power spectral density estimates integrate seamlessly into engineering workflows.

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