Ferdinand Fuhrmann

534 total citations
20 papers, 299 citations indexed

About

Ferdinand Fuhrmann is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Artificial Intelligence. According to data from OpenAlex, Ferdinand Fuhrmann has authored 20 papers receiving a total of 299 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Computer Vision and Pattern Recognition, 11 papers in Signal Processing and 6 papers in Artificial Intelligence. Recurrent topics in Ferdinand Fuhrmann's work include Music and Audio Processing (10 papers), Music Technology and Sound Studies (8 papers) and Speech and Audio Processing (7 papers). Ferdinand Fuhrmann is often cited by papers focused on Music and Audio Processing (10 papers), Music Technology and Sound Studies (8 papers) and Speech and Audio Processing (7 papers). Ferdinand Fuhrmann collaborates with scholars based in Austria, Spain and United Kingdom. Ferdinand Fuhrmann's co-authors include Perfecto Herrera, Martín Haro, Dmitry Bogdanov, Anna Xambó, Emília Gómez, Jordi Janer, René Kaiser, Giuseppe Riccardi, Alexandros Potamianos and Elisabeth André and has published in prestigious journals such as Energies, Alzheimer s & Dementia and Information Processing & Management.

In The Last Decade

Ferdinand Fuhrmann

19 papers receiving 275 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Ferdinand Fuhrmann Austria 8 164 153 78 58 43 20 299
Tristan Jehan United States 9 156 1.0× 147 1.0× 38 0.5× 23 0.4× 68 1.6× 19 244
Keiichiro Hoashi Japan 13 161 1.0× 247 1.6× 130 1.7× 91 1.6× 25 0.6× 46 463
Luís Gustavo Martins Portugal 8 383 2.3× 319 2.1× 102 1.3× 47 0.8× 121 2.8× 17 518
Noris Mohd Norowi Malaysia 6 58 0.4× 60 0.4× 77 1.0× 51 0.9× 16 0.4× 47 222
Matthew Yee-King United Kingdom 8 66 0.4× 105 0.7× 65 0.8× 13 0.2× 47 1.1× 35 226
Alicja Wieczorkowska Poland 9 107 0.7× 71 0.5× 45 0.6× 27 0.5× 37 0.9× 26 208
Hiromu Yakura Japan 8 102 0.6× 38 0.2× 72 0.9× 53 0.9× 16 0.4× 22 199
Hamid Eghbal-zadeh Austria 9 187 1.1× 135 0.9× 132 1.7× 83 1.4× 29 0.7× 20 323
Erno Mäkinen Finland 8 164 1.0× 340 2.2× 64 0.8× 28 0.5× 32 0.7× 14 465
Deepanway Ghosal Singapore 12 147 0.9× 184 1.2× 441 5.7× 47 0.8× 21 0.5× 22 673

Countries citing papers authored by Ferdinand Fuhrmann

Since Specialization
Citations

This map shows the geographic impact of Ferdinand Fuhrmann'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 Ferdinand Fuhrmann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ferdinand Fuhrmann more than expected).

Fields of papers citing papers by Ferdinand Fuhrmann

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ferdinand Fuhrmann. 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 Ferdinand Fuhrmann. The network helps show where Ferdinand Fuhrmann may publish in the future.

Co-authorship network of co-authors of Ferdinand Fuhrmann

This figure shows the co-authorship network connecting the top 25 collaborators of Ferdinand Fuhrmann. A scholar is included among the top collaborators of Ferdinand Fuhrmann based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ferdinand Fuhrmann. Ferdinand Fuhrmann is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Fuhrmann, Ferdinand, et al.. (2024). 3D Acoustic Heat-Maps for Transformer Monitoring Applications. Journal of Energy - Energija. 72(4). 15–18. 2 indexed citations
2.
Pestana, Jesús, et al.. (2024). First Measurement Campaign by a Multi-Sensor Robot for the Lifecycle Monitoring of Transformers. Energies. 17(5). 1152–1152. 2 indexed citations
3.
Fuhrmann, Ferdinand, et al.. (2023). X-AMINOR: A Mobile Multi-Sensor Platform for Lifecycle-Monitoring of Transformers. 121–128. 1 indexed citations
4.
Fuhrmann, Ferdinand, et al.. (2021). Multimodal Interaction in the Production Line - An OPC UA-based Framework for Injection Molding Machinery. 837–838. 2 indexed citations
5.
Graf, Franz, et al.. (2021). Let’s teach computers to listen. e+i Elektrotechnik und Informationstechnik. 138(3). 139–147. 1 indexed citations
6.
Paletta, Lucas, et al.. (2020). Virtual reality–based mindfulness training, sensory activation and mental assessment in dementia care. Alzheimer s & Dementia. 16(S7). 6 indexed citations
7.
8.
Fuhrmann, Ferdinand, et al.. (2017). Towards an Architecture for collaborative Human Robot Interaction in Physiotherapeutic Applications. 319–320. 2 indexed citations
9.
Vinciarelli, Alessandro, Anna Esposito, Elisabeth André, et al.. (2015). Open Challenges in Modelling, Analysis and Synthesis of Human Behaviour in Human–Human and Human–Machine Interactions. Cognitive Computation. 7(4). 397–413. 61 indexed citations
10.
Kaiser, René & Ferdinand Fuhrmann. (2014). Multimodal Interaction for Future Control Centers. 47–51. 1 indexed citations
11.
Fuhrmann, Ferdinand & René Kaiser. (2014). Multimodal Interaction for Future Control Centers. 66–67. 3 indexed citations
12.
Bogdanov, Dmitry, Martín Haro, Ferdinand Fuhrmann, et al.. (2012). Semantic audio content-based music recommendation and visualization based on user preference examples. Information Processing & Management. 49(1). 13–33. 83 indexed citations
13.
Janer, Jordi, et al.. (2012). A Comparison Of Sound Segregation Techniques For Predominant Instrument Recognition In Musical Audio Signals.. Zenodo (CERN European Organization for Nuclear Research). 559–564. 54 indexed citations
14.
Fuhrmann, Ferdinand & Perfecto Herrera. (2011). Quantifying The Relevance Of Locally Extracted Information For Musical Instrument Recognition From Entire Pieces Of Music.. Zenodo (CERN European Organization for Nuclear Research). 239–244. 1 indexed citations
15.
Bogdanov, Dmitry, Martín Haro, & Ferdinand Fuhrmann. (2011). A content-based system for music recommendation and visualization of user preferences working on semantic notions. Repositori digital de la UPF (Universitat Pompeu Fabra). 249–252. 8 indexed citations
16.
Bogdanov, Dmitry, et al.. (2010). Content-based music recommendation based on user preference examples. Repositori digital de la UPF (Universitat Pompeu Fabra). 26 indexed citations
17.
Haro, Martín, Anna Xambó, Ferdinand Fuhrmann, et al.. (2010). TheMusical Avatar. Repositori digital de la UPF (Universitat Pompeu Fabra). 1–8. 11 indexed citations
18.
Fuhrmann, Ferdinand & Perfecto Herrera. (2010). Polyphonic Instrument Recognition for exploring semantic Similarities in Music. 7 indexed citations
19.
Fuhrmann, Ferdinand, Martín Haro, & Perfecto Herrera. (2009). Scalability, Generality And Temporal Aspects In Automatic Recognition Of Predominant Musical Instruments In Polyphonic Music.. Zenodo (CERN European Organization for Nuclear Research). 321–326. 16 indexed citations
20.
Fuhrmann, Ferdinand, Perfecto Herrera, & Xavier Serra. (2009). Detecting Solo Phrases in Music Using Spectral and Pitch-related Descriptors. Journal of New Music Research. 38(4). 343–356. 2 indexed citations

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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2026