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concept#Data#Analytics#Software Engineering

Signal Preprocessing

Systematic preparation of raw signals by cleaning, normalizing and transforming them to provide reliable inputs for analysis or processing stages.

Signal preprocessing involves cleaning, normalizing and transforming raw signals before analysis or algorithmic use.
Established
Medium

Classification

  • Medium
  • Technical
  • Design
  • Intermediate

Technical context

Ingestion layer (Kafka, MQTT)Feature store or data lakeML training and inference pipelines

Principles & goals

Preprocessing prioritizes data quality before feature engineering.Transparent, reproducible steps and metadata ensure traceability.Techniques must be chosen and validated per domain.
Build
Domain, Team

Use cases & scenarios

Compromises

  • Overfitting of data due to aggressive filtering
  • Lack of documentation leads to inconsistent pipelines
  • Loss of rare but relevant events
  • Version preprocessing scripts and parameters
  • Use reproducible pipelines and test data
  • Define quality metrics and acceptance criteria

I/O & resources

  • Raw signals (time-series, multichannel)
  • Metadata and calibration data
  • Recording/sampling specifications
  • Cleaned, normalized time series
  • Extracted features and quality metrics
  • Processing metadata (version, parameters)

Description

Signal preprocessing involves cleaning, normalizing and transforming raw signals before analysis or algorithmic use. It reduces noise, corrects measurement errors and extracts relevant features. Common techniques include filtering, resampling, windowing and feature scaling to provide consistent, comparable inputs for analytics and signal-based applications.

✔

  • Reduced false alarms and more stable analyses
  • Improved comparability and reproducibility
  • Higher efficiency downstream in models and algorithms

✖

  • Preprocessing can remove relevant signal information if misparameterized
  • Computational cost and latency in real-time scenarios
  • Ad-hoc solutions reduce maintainability without standardization

  • Signal-to-Noise Ratio (SNR)

    Measures the ratio of signal to noise after preprocessing.

  • Error rate / false alarms

    Proportion of erroneous or falsely flagged events.

  • Processing latency

    Average time to preprocess per message/time window.

Vibration analysis in manufacturing

Preprocessing removes frequency components and noise, extracts peaks for condition monitoring and reduces false alarms.

ECG signal cleaning in healthcare

Baseline correction and artifact removal improve detection of cardiac arrhythmias.

Audio normalization for speech models

Volume adjustment and spectral features lead to more robust speech recognition across recording conditions.

1

Analyze raw data and define quality goals

2

Select appropriate filtering and normalization methods

3

Implement in ingest or batch pipeline with monitoring

4

Validate with test datasets and document

⚠️ Technical debt & bottlenecks

  • Hard-coded filter parameters in production scripts
  • Missing tests for edge cases and rare events
  • Inconsistent metadata across data sources
Compute on edge devicesBandwidth and network latencyLack of standardization for sensor metadata
  • Aggressive low-pass filtering removes signal spikes that represent anomalies
  • Resampling without anti-aliasing causes distortions
  • Normalizing over entire dataset prevents online processing
  • Ignored time offsets between channels
  • Hidden dependency on calibration data
  • Tuning parameters only on training data without validation
Fundamentals of digital signal processingKnowledge of sampling, filter design and spectral analysisExperience with data pipelines and measurement data
Real-time capability and latency requirementsData quality and traceabilityScalability of preprocessing pipelines
  • • Real-time requirements limit batch methods
  • • Compute and memory limits on edge/embedded
  • • Regulatory requirements for handling measurement data