Science Explorer Interactive view Map

Music and Audio Processing

Music and Audio Processing is a research topic within Signal Processing. Science Explorer counts 37k research works in it since 1950. 17.4% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the classification and analysis of audio signals, including music genre classification, environmental sound recognition, melody extraction, and acoustic scene classification. It explores techniques such as deep learning, convolutional neural networks, and feature extraction for music information retrieval.

  • Audio Signal Classification
  • Music Information Retrieval
  • Deep Learning
  • Convolutional Neural Networks
  • Feature Extraction
  • Environmental Sound Recognition
  • Music Genre Classification
  • Melody Extraction
  • Acoustic Scene Classification
  • Audio Event Detection
Research works
37k
fractional, since 1950
In the world top 10%
6.4k
per year above
Top-10% rate
17.4%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
+35%
the tick is no change

Which countries lead Music and Audio Processing research?

By volume, China and India publish the most (2.2k and 1.1k works in 2022–2025).

By volume, 2022–2025

  1. 1 China 2.2k works
  2. 2 India 1.1k works
  3. 3 United States 1k works
  4. 4 Japan 414 works
  5. 5 United Kingdom 340 works
  6. 6 South Korea 262 works
  7. 7 Germany 257 works
  8. 8 France 228 works
  9. 9 Italy 186 works
  10. 10 Canada 161 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: 35.2%India: 18.1%United States: 16.6%Japan: 6.7%6 others listed: 23.4%35%largest
China2,164 · 35.2%India1,110 · 18.1%United States1,021 · 16.6%Japan414 · 6.7%6 others listed1,435 · 23.4%

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

Which institutions lead Music and Audio Processing research?

By volume in 2022–2025, Medical Library Association publishes the most Music and Audio Processing research, followed by Shanghai Jiao Tong University and University of Science and Technology of China.

Who are the leading researchers in Music and Audio Processing?

The most-cited researchers publishing on Music and Audio Processing include Andrew Zisserman, Geoffrey E. Hinton and Yoshua Bengio.

  1. 1 Andrew Zisserman United Kingdom 25k citations
  2. 2 Geoffrey E. Hinton Canada 22k citations
  3. 3 Yoshua Bengio Canada 17k citations
  4. 4 Wei Liu China 9.7k citations
  5. 5 Luc Van Gool Switzerland 8.5k citations
  6. 6 Thomas S. Huang United States 7.4k citations
  7. 7 Trevor Darrell United States 6.7k citations

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

Where is Music and Audio Processing research done?

The largest centres of Music and Audio Processing research in 2022–2025 are Beijing (China), Tokyo (Japan), Shanghai (China) and Seoul (South Korea). Among places with at least 20 works in it, it is an unusually large share of all research in Guildford.

Largest cities, 2022–2025

  1. 1 Beijing China 435 works
  2. 2 Tokyo Japan 183 works
  3. 3 Shanghai China 149 works
  4. 4 Seoul South Korea 128 works
  5. 5 London United Kingdom 109 works
  6. 6 Xi'an China 108 works
  7. 7 Chennai India 107 works
  8. 8 Shenzhen China 85 works
  9. 9 Bengaluru India 84 works
  10. 10 Hangzhou China 83 works

Where it is the local speciality

  1. GuildfordGB · 22.7 works9.5×
← less than its size predictsmore →

Location quotient: how much more of its research is in Music and Audio Processing than the world average.

See Music and Audio Processing on the map

Where is the best place to study Music and Audio Processing?

Among universities, judged by research, University of Surrey, Carnegie Mellon University and Queen Mary University of London 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 24.87%fractional works in this node (log) →share in the world top 10% →University of Surrey: 23, 29.6%Carnegie Mellon University: 31, 29.3%Queen Mary University of London: 36, 16.7%Shanghai Jiao Tong University: 49, 25.3%Amrita Vishwa Vidyapeetham: 41, 9.8%The University of Texas at Dallas: 16, 26.1%Korea Advanced Institute of Science and Technology: 29, 25.7%Nanyang Technological University: 25, 35.1%University of Science and Technology of China: 47, 25.7%Johannes Kepler University of Linz: 12, 25.4%University of SurreyCarnegie Mellon Univ…Shanghai Jiao Tong U…Queen Mary Universit…
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1 University of SurreyUnited Kingdom 81.129.6%11.0×23 +297.3%
2 Carnegie Mellon UniversityUnited States 67.229.3%9.4×31 +8.8%
3 Queen Mary University of LondonUnited Kingdom 65.416.7%11.2×36 +50.8%
4 Shanghai Jiao Tong UniversityChina 61.325.3%2.8×49 +253.0%
5 Amrita Vishwa VidyapeethamIndia 61.09.8%9.3×41 +164.3%
6 The University of Texas at DallasUnited States 59.826.1%9.6×16 +30.3%
7 Korea Advanced Institute of Science and TechnologySouth Korea 59.025.7%7.6×29 +12.4%
8 Nanyang Technological UniversitySingapore 58.535.1%3.8×25 -20.9%
9 University of Science and Technology of ChinaChina 58.325.7%4.8×47 +62.5%
10 Johannes Kepler University of LinzAustria 57.825.4%8.8×12 +60.6%

Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.

Is Music and Audio Processing research growing?

Output in 2018–2022 was 35% higher than in 2013–2017, peaking in 2023. The fastest-growing topics are Music and Audio Processing.

19801990200020102020
grewheldshrank

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

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.