ICALAB for Signal Processing - benchmarks

ICALAB package contains several benchmarks for testing and comparing performance of the implemented algorithms.

As you will see, there is no universal algorithm that can successfully separate all sources included in these benchmarks

The most interesting benchmarks are briefly described below.

  1. ACsin10d.mat (mat - file, ascii - gzipped file)- contains 10 sine wave sources of the following form:

    sn = sin ((2n-1) w k)     for     n = 1,2,....,10.

    The sources can be easily separated by second order statistics methods (SOS) like AMUSE, EVD or SOBI algorithms. However, higher order statistics (HOS) ICA algorithms fail to reconstruct such sources because they are dependent. It is interesting to note that different ICA algorithms (for example JADE and Natural Gradient algorithms) give usually different (inconsistent) results ("independent components") for this benchmark.

    ACsin4d.mat(mat - file, ascii - gzipped file) benchmark is similar to ACsin10d.mat but contains only 4 sources.

  2. ACsparse10.mat (mat - file, ascii - gzipped file) - contains 10 sparse (smooth bell-shape) sources that are approximately independent. The SOS blind source separation algorithms fail to separate such sources. Also, some ICA algorithms have failed to separate such sources. Please try ICALAB to compare performance of various algorithms.
  3. ACvsparse10.mat (mat - file, ascii - gzipped file) - contains 10 very sparse (short regular pulses) signals. Second order BSS algorithms fail to separate these sources.
  4. ABio7.mat (mat - file, ascii - gzipped file) - this benchmark contains 7 typical biological sources. This benchmark was proposed by Allan Barros.
  5. Sergio7.mat (mat - file, ascii - gzipped file) - this benchmark contains 7 sources (some of them are asymmetrically distributed). This benchmark was proposed by Sergio Cruces.
  6. AC10-7sparse.mat (mat - file, ascii - gzipped file) - contains 10 sensor signals which are mixtures of 7 sources (extracted from the file ACsparse10.mat).
  7. acspeech16.mat (mat - file, ascii - gzipped file) - contains 16 typical speech signals which have a temporal structure but are not precisely independent.
  8. Similar benchmarks are:

  9. Speech4.mat (mat - file, ascii - gzipped file), Speech8.mat (mat - file, ascii - gzipped file), Speech10.mat (mat - file, ascii - gzipped file) and Speech20.mat (mat - file, ascii - gzipped file) are benchmarks with 4, 8, 10 and 20 sounds (speech and music) sources.
  10. 10halo.mat (mat - file, ascii - gzipped file) - contains 10 speech signals that are highly correlated (all 10 speakers say the same sentence).
  11. nband5.mat (mat - file, ascii - gzipped file) - contains 5 narrow band sources. This is a rather "easy" benchmark. Please try any second order statistics (SOS) algorithm like SOBI, SOBI-RO or AMUSE.
  12. Gnband.mat (mat - file, ascii - gziped file) - contains 5 fourth order colored sources with a distribution close to Gaussian. This is a rather "difficult" benchmark. Please try program JADE-TD to separate signals from their mixture.
  13. EEG19.mat (mat - file, ascii - gzipped file) - consists 19 EEG signals with clear heart, eye movement and eye blinking artifacts.


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