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Topic · Signal Processing

Blind Source Separation Techniques

Blind Source Separation Techniques is a research topic within Signal Processing. Science Explorer counts 43k research works in it since 1950. 9.8% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on blind source separation and independent component analysis, with applications in signal decomposition, biomedical signals, and processing of convolutive mixtures. The methods often utilize sparse representation and exploit the temporal structure of the signals, particularly in the frequency domain.

  • Independent Component Analysis
  • Blind Separation
  • Sparse Representation
  • Signal Decomposition
  • Convolutional Mixtures
  • Biomedical Signals
  • Frequency Domain
  • Non-stationary Sources
  • Natural Images
  • Temporal Structure
Research works
43k
fractional, since 1950
In the world top 10%
4.2k
per year above
Top-10% rate
9.8%
share of its works in the world top 10%
Growth, 2018-21 → 2022-25
-13%
the tick is no change
Largest producer
China
53% of the 10 largest, 2022-now
Blind Source Separation Techniques

Which countries lead Blind Source Separation Techniques research?

By volume, China and India publish the most (2k and 545 works in 2022-now).

By volume, 2022-now

  1. 1 China 2k works
  2. 2 India 545 works
  3. 3 United States 452 works
  4. 4 Japan 169 works
  5. 5 France 127 works
  6. 6 Germany 108 works
  7. 7 United Kingdom 101 works
  8. 8 Iran 82 works
  9. 9 South Korea 82 works
  10. 10 Italy 79 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

China: 52.9%India: 14.7%United States: 12.2%Japan: 4.6%6 others listed: 15.6%53%largest
China1,956 · 52.9%India545 · 14.7%United States452 · 12.2%Japan169 · 4.6%6 others listed578 · 15.6%

Shares of the rows listed above, not of the whole node.

Which institutions lead Blind Source Separation Techniques research?

By volume in 2022-now, Chinese Academy of Sciences publishes the most Blind Source Separation Techniques research, followed by University of Electronic Science and Technology of China and Xidian University.

By volume, 2022-now

  1. 1 Chinese Academy of SciencesChina 65 works
  2. 2 University of Electronic Science and Technology of ChinaChina 58 works
  3. 3 Xidian UniversityChina 44 works
  4. 4 Northwestern Polytechnical UniversityChina 43 works
  5. 5 Centre National de la Recherche ScientifiqueFrance 40 works
  6. 6 Harbin Engineering UniversityChina 39 works
  7. 7 National University of Defense TechnologyChina 33 works
  8. 8 Xi'an Jiaotong UniversityChina 32 works
  9. 9 Harbin Institute of TechnologyChina 32 works
  10. 10 Southeast UniversityChina 30 works

Who are the leading researchers in Blind Source Separation Techniques?

The most-cited researchers publishing on Blind Source Separation Techniques include Yoshua Bengio, Geoffrey E. Hinton and Robert John Tibshirani.

  1. 1 Yoshua Bengio Canada 476k citations
  2. 2 Geoffrey E. Hinton Canada 454k citations
  3. 3 Robert John Tibshirani United States 396k citations
  4. 4 Xiangyu Zhang 327k citations
  5. 5 Karl John Friston United Kingdom 310k citations
  6. 6 Trevor Hastie United States 301k citations
  7. 7 Xiaogang Wang China 260k citations
  8. 8 Yann LeCun United States 256k citations
  9. 9 Andrew Zisserman United Kingdom 255k citations
  10. 10 Gordon K. Smyth Australia 237k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

What are the most cited papers in Blind Source Separation Techniques?

The most cited paper in Blind Source Separation Techniques is "Support-Vector Networks" (Corinna Cortes, Vladimir N. Vapnik, 1995), with 34,309 citations. Next come "Learning representations by back-propagating errors" (32,003) and "EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis" (25,692).

Most cited papers
#PaperReleasedCitations
1 Support-Vector Networks Corinna Cortes, Vladimir N. Vapnik · Machine Learning Open access Sep 1995 34,309
2 Learning representations by back-propagating errors David E. Rumelhart, Geoffrey E. Hinton, Ronald J. Williams · Nature Oct 1986 32,003
3 EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis Arnaud Delorme, Scott Makeig · Journal of Neuroscience Methods 5 Feb 2004 25,692
4 Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers Stephen Boyd, Neal Parikh, Eric Chu et al. · Foundations and Trends® in Machine Learning 26 Jul 2011 16,474
5 A Global Geometric Framework for Nonlinear Dimensionality Reduction Joshua B. Tenenbaum, Vin de Silva, John C. Langford · Science 22 Dec 2000 13,882
All 100 most cited papers, with filters and sorting

Where is Blind Source Separation Techniques research done?

The largest centres of Blind Source Separation Techniques research in 2022-now are Beijing (China), Xi'an (China), Chengdu (China) and Nanjing (China). Among places with at least 20 works in it, it is an unusually large share of all research in Xi'an and Harbin.

Largest cities, 2022-now

  1. 1 BeijingChina 325 works
  2. 2 Xi'anChina 180 works
  3. 3 ChengduChina 126 works
  4. 4 NanjingChina 115 works
  5. 5 ShanghaiChina 109 works
  6. 6 HarbinChina 81 works
  7. 7 TokyoJapan 80 works
  8. 8 GuangzhouChina 78 works
  9. 9 HangzhouChina 74 works
  10. 10 ChongqingChina 63 works

Where it is the local speciality

  1. Xi'anCN · 180.4 works5.0×
  2. HarbinCN · 81.2 works4.3×
← less than its size predicts1×more →

Location quotient: how much more of its research is in Blind Source Separation Techniques than the world average.

See Blind Source Separation Techniques on the map

Where is the best place to study Blind Source Separation Techniques?

Among universities, judged by research, Northwestern Polytechnical University, Xidian University and Indian Institute of Technology Indore score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%20%40%mean 20.96%fractional works in this field (log) →share of work in the world top 10% →Indian Institute of …Southeast UniversityNorthwestern Polytec…Xidian University
above the meannear itbelow it

One dot per university in the first ten of the table below, at the default weights. The upper left is the interesting corner: small places doing unusually strong work.

Excellenceshare of its work in the world top 10%, 2022-now and all time averaged
40%
Specialisationhow much of its work is in this field, counted up to 10× the world share
10%
Sizeits output in this field, on a log scale
30%
Growthoutput growth, counted from -20% to +20%
10%
Internationalshare of its work with a co-author abroad
10%
Each slider sets a weight; the share it gets of the score is beside it.
#UniversityScoreTop 10%SpecialisationWorksGrowth
1Northwestern Polytechnical UniversityChina 57.815.7%9.2×43 +36.1%
2Xidian UniversityChina 54.810.3%14.6×44 +38.8%
3Indian Institute of Technology IndoreIndia 53.842.0%12.4×9 -34.2%
4Southeast UniversityChina 52.918.0%4.9×30 +17.5%
5University of Electronic Science and Technology of ChinaChina 52.28.7%12.6×58 -4.4%
6Hefei University of TechnologyChina 50.637.6%4.4×10 +131.8%
7Xi’an University of Posts and TelecommunicationsChina 50.429.9%15.2×13 +84.9%
8Zhejiang UniversityChina 47.815.4%2.4×27 +22.9%
9University of Science and Technology of ChinaChina 46.020.8%3.8×18 +38.6%
10Harbin Engineering UniversityChina 45.711.2%18.2×39 -5.6%

Universities only. The score blends excellence (40%), size (30%), specialisation (10%), growth (10%), international reach (10%), 2022-now; growth compares 2018-21 with 2022-25 and counts up to +20%, since a jump from a small base is mostly noise.

Every institution in Blind Source Separation Techniques, country by country

Is Blind Source Separation Techniques research growing?

Output in 2022-2025 was 13% lower than in 2018-2021, peaking in 2002.

still counting19801990200020102020
grewheldshrank

The same series as a ribbon: one cell per year, darker for more. The line above answers how much; this answers when.

Its share of all research

Growing output can still be a shrinking share: all of science grows. This is Blind Source Separation Techniques as a percentage of every work published that year, 0.022% in 2024.

still counting0.10%0.20%Blind Source Separation Techniques0.017% in 202519801990200020102020

Fractional works of Blind Source Separation Techniques over all fractional works that year. How the balance of science has shifted.

How much of it reaches the world top 10%

Year by year.

still counting5%10%15%Blind Source Separation Techniques9.7% in 2025200020102020

Of each year's works in it, the share that reached the world's 10% most cited for their field and year. A field is the world for its own topics, so there is no other line to hold it against.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.

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