Bernhard Lehner

725 total citations
26 papers, 498 citations indexed

About

Bernhard Lehner is a scholar working on Signal Processing, Artificial Intelligence and Mechanics of Materials. According to data from OpenAlex, Bernhard Lehner has authored 26 papers receiving a total of 498 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Signal Processing, 8 papers in Artificial Intelligence and 6 papers in Mechanics of Materials. Recurrent topics in Bernhard Lehner's work include Music and Audio Processing (11 papers), Speech and Audio Processing (10 papers) and Speech Recognition and Synthesis (8 papers). Bernhard Lehner is often cited by papers focused on Music and Audio Processing (11 papers), Speech and Audio Processing (10 papers) and Speech Recognition and Synthesis (8 papers). Bernhard Lehner collaborates with scholars based in Austria, Germany and Hungary. Bernhard Lehner's co-authors include Gerhard Widmer, Emeka Nkenke, Friedrich Wilhelm Neukam, Martin Radespiel‐Tröger, Ulf Thams, Jan Schlüter, Helmut Steveling, Jörg Neugebauer, Sebastian Böck and Gerhard G. Grabenbauer and has published in prestigious journals such as Journal of Applied Physics, International Journal of Radiation Oncology*Biology*Physics and Sensors.

In The Last Decade

Bernhard Lehner

23 papers receiving 474 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Bernhard Lehner Austria 13 163 144 92 84 81 26 498
Christoph Runte Germany 13 13 0.1× 218 1.5× 37 0.4× 16 0.2× 103 1.3× 35 591
Kris Verstreken Belgium 10 10 0.1× 347 2.4× 37 0.4× 18 0.2× 177 2.2× 16 638
Kailai Zhang China 11 18 0.1× 296 2.1× 54 0.6× 119 1.4× 191 2.4× 26 575
Sven De Greef Belgium 11 9 0.1× 263 1.8× 220 2.4× 13 0.2× 81 1.0× 19 835
Erwin Keeve Germany 18 15 0.1× 478 3.3× 207 2.3× 8 0.1× 292 3.6× 45 1.1k
M. Bock United Kingdom 12 8 0.0× 89 0.6× 73 0.8× 13 0.2× 67 0.8× 20 697
Shenghui Liao China 15 5 0.0× 324 2.3× 64 0.7× 29 0.3× 230 2.8× 55 724
Valentina Pucciarelli Italy 14 9 0.1× 110 0.8× 47 0.5× 7 0.1× 50 0.6× 38 557
Felix Kunz Germany 14 11 0.1× 178 1.2× 61 0.7× 62 0.7× 63 0.8× 32 574
Kento Odaka Japan 13 4 0.0× 199 1.4× 120 1.3× 20 0.2× 113 1.4× 50 544

Countries citing papers authored by Bernhard Lehner

Since Specialization
Citations

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

Fields of papers citing papers by Bernhard Lehner

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Bernhard Lehner

This figure shows the co-authorship network connecting the top 25 collaborators of Bernhard Lehner. A scholar is included among the top collaborators of Bernhard Lehner 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 Bernhard Lehner. Bernhard Lehner 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.
2.
Schuster, Stefan, et al.. (2024). Threshold Effect of Neural Network-Based Fundamental Frequency Estimators. 1291–1295.
3.
4.
Lehner, Bernhard, et al.. (2022). Acoustic Temperature Tomography using a UNet based Deep Learning Approach. 2022 30th European Signal Processing Conference (EUSIPCO). 1561–1565.
5.
Kovács, Péter, et al.. (2020). Deep learning approaches for thermographic imaging. Journal of Applied Physics. 128(15). 28 indexed citations
6.
Humphrey, Eric J., Sravana Reddy, Prem Seetharaman, et al.. (2018). An Introduction to Signal Processing for Singing-Voice Analysis: High Notes in the Effort to Automate the Understanding of Vocals in Music. IEEE Signal Processing Magazine. 36(1). 82–94. 23 indexed citations
7.
Lehner, Bernhard, Jan Schlüter, & Gerhard Widmer. (2018). Online, Loudness-Invariant Vocal Detection in Mixed Music Signals. IEEE/ACM Transactions on Audio Speech and Language Processing. 26(8). 1369–1380. 20 indexed citations
8.
Schlüter, Jan & Bernhard Lehner. (2018). Zero-Mean Convolutions for Level-Invariant Singing Voice Detection. Zenodo (CERN European Organization for Nuclear Research). 321–326. 19 indexed citations
9.
Lehner, Bernhard & Gerhard Widmer. (2015). Monaural Blind Source Separation in the Context of Vocal Detection.. International Symposium/Conference on Music Information Retrieval. 309–315. 4 indexed citations
10.
Lehner, Bernhard, et al.. (2015). Improving voice activity detection in movies. 2942–2946. 5 indexed citations
11.
Eghbal-zadeh, Hamid, Bernhard Lehner, Markus Schedl, & Gerhard Widmer. (2015). I-VECTORS FOR TIMBRE-BASED MUSIC SIMILARITY AND MUSIC ARTIST CLASSIFICATION. Zenodo (CERN European Organization for Nuclear Research). 554–560. 9 indexed citations
12.
Lehner, Bernhard, et al.. (2014). On the reduction of false positives in singing voice detection. 7480–7484. 32 indexed citations
13.
Nkenke, Emeka, Bernhard Lehner, Manuel Kramer, et al.. (2006). Determination of Facial Symmetry in Unilateral Cleft Lip and Palate Patients from Three-Dimensional Data: Technical Report and Assessment of Measurement Errors. The Cleft Palate-Craniofacial Journal. 43(2). 129–137. 51 indexed citations
14.
Schultze–Mosgau, Stefan, Bernhard Lehner, Franz Rödel, et al.. (2005). Expression of bone morphogenic protein 2/4, transforming growth factor-β1, and bone matrix protein expression in healing area between vascular tibia grafts and irradiated bone—experimental model of osteonecrosis. International Journal of Radiation Oncology*Biology*Physics. 61(4). 1189–1196. 34 indexed citations
15.
Nkenke, Emeka, Bernhard Lehner, Manuel Kramer, et al.. (2005). Determination Of Facial Symmetry In Unilateral Cleft Lip And Palate Patients From 3D Data: Technical Report And Assessment Of Measurement Errors. The Cleft Palate-Craniofacial Journal. 1 indexed citations
16.
Nkenke, Emeka, Bernhard Lehner, Matthias Fenner, et al.. (2005). Immediate versus delayed loading of dental implants in the maxillae of minipigs: follow-up of implant stability and implant failures.. PubMed. 20(1). 39–47. 45 indexed citations
17.
Lehner, Bernhard, Jan S. Bauer, Franz Rödel, et al.. (2004). Radiation-induced impairment of osseous healing with vascularized bone transfer: experimental model using a pedicled tibia flap in rat. International Journal of Oral and Maxillofacial Surgery. 33(5). 486–492. 11 indexed citations
18.
Nkenke, Emeka, Bernhard Lehner, Ulf Thams, et al.. (2003). Bone contact, growth, and density around immediately loaded implants in the mandible of mini pigs. Clinical Oral Implants Research. 14(3). 312–321. 109 indexed citations
19.
Lehner, Bernhard, et al.. (2003). Influence of Early Hard Palate Closure in Unilateral and Bilateral Cleft Lip and Palate on Maxillary Transverse Growth During the First Four Years of Age. The Cleft Palate-Craniofacial Journal. 40(2). 126–130. 20 indexed citations
20.
Lehner, Bernhard, et al.. (1987). [Paraocclusal axiography: the protrusion pathway in subjects with complete dentition--a clinico-experimental study].. PubMed. 97(4). 438–48. 4 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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