Welcome to Multi-Component Signal Methods Benchmarks’s documentation!

A Common Framework for Benchmarking of MCS Methods

We introduce a public, open-source, Python-based toolbox for benchmarking multi-component signal analysis methods, implemented either in Python or Matlab.

The goal of this toolbox is providing the signal-processing community with a common framework that allows researcher-independent comparisons between methods and favors reproducible research.

Contents:

• Installation
• What is mcsm-benchs?
• Architecture and basic functionality
• The SignalBank class
• The ResultsInterpreter class
• Extra functionalities
• Collaborative benchmarks

Examples:

• mcsm-benchs: Creating benchmarks of MCS Methods
• mcsm-benchs: Exploring signals provided by the SignalBank class
• mcsm-benchs: Benchmarks with personalized noise-generating functions
• mcsm-benchs: Querying Signal class attributes for more versatile benchmarks
• mcsm-benchs: Using user-provided signals and performance metric

Indices and tables

• Index

• Module Index

• Search Page