Ming Tu

983 citations
35 papers · 377 · h-index 10

Impact in

Papers in

    • Speech and Audio Processing 16
    • Music and Audio Processing 9
    • Blind Source Separation Techniques 4
    • Speech Recognition and Synthesis 10
    • Neural Networks and Applications 2

Ming Tu

34 papers receiving 355 citations

Peers

Ming Tu
Comparison fields: 5 of 82
  • Signal Processing 145
  • Artificial Intelligence 238
  • Experimental and Cognitive Psychology 78
  • Physiology 66
  • Computer Vision and Pattern Recognition 44
Replace Georg Stemmer with:
Georg Stemmer Germany
Miloš Cerňak Switzerland
Tamás Gábor Csapó Hungary
Mehmet Bilal Er Türkiye
Krzysztof Marasek Poland
S. Pravin Kumar India
Phani Sankar Nidadavolu United States
Harald Höge Germany
Sorin Dusan United States
Xingyu Cai United States
Ming Tu relative to Georg Stemmer Germany Georg Stemmer's profile →
Citations per field
00.5×3.6×
Georg Stemmer · 1×
Citations per year

Countries citing papers authored by Ming Tu

Since Specialization
Citations

This map shows the geographic impact of Ming Tu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ming Tu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ming Tu more than expected).

Fields of papers citing papers by Ming Tu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ming Tu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ming Tu. The network helps show where Ming Tu may publish in the future.

Co-authors

The 25 scholars most cited alongside Ming Tu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Ming Tu Line = papers co-authored together Ming Tu links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 35 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202065
2 201641
3 202336
4 201734
5 202034
6 201627
7 201824
8 201723
9 201516
10 201610
11 20199
12 20168
13 20175
14 20164
15 20234
16 20144
17 20173
18 20203
19 20143
20 20163

About Ming Tu

Ming Tu is a scholar working on Signal Processing, Artificial Intelligence, Computational Mechanics, Computer Vision and Pattern Recognition and Experimental and Cognitive Psychology, having authored 35 papers that have together received 377 indexed citations. Recurring topics across this work include Speech and Audio Processing (16 papers), Speech Recognition and Synthesis (10 papers), Music and Audio Processing (9 papers), Advanced Adaptive Filtering Techniques (5 papers), Blind Source Separation Techniques (4 papers), Phonetics and Phonology Research (2 papers), Neural Networks and Applications (2 papers) and Voice and Speech Disorders (2 papers). The work is most often cited by research in Signal Processing (145 citations), Artificial Intelligence (238 citations), Experimental and Cognitive Psychology (78 citations), Physiology (66 citations) and Computer Vision and Pattern Recognition (44 citations). Ming Tu has collaborated with scholars based in United States, China and Sweden. Frequent co-authors include Visar Berisha, Julie Liss, Xianxian Zhang, Kevin Huang, Jing Huang, Guangtao Wang, Xiaodong He, Bowen Zhou, Haitao Chen and Shrikanth Narayanan. Their work appears in journals such as Dyes and Pigments, International journal of agricultural and biological engineering, Buildings, Energies and International Journal of Molecular Sciences.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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