Arne Leijon

2.7k total citations · 1 hit paper
88 papers, 1.9k citations indexed

About

Arne Leijon is a scholar working on Signal Processing, Cognitive Neuroscience and Artificial Intelligence. According to data from OpenAlex, Arne Leijon has authored 88 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 47 papers in Signal Processing, 38 papers in Cognitive Neuroscience and 26 papers in Artificial Intelligence. Recurrent topics in Arne Leijon's work include Speech and Audio Processing (44 papers), Hearing Loss and Rehabilitation (38 papers) and Noise Effects and Management (18 papers). Arne Leijon is often cited by papers focused on Speech and Audio Processing (44 papers), Hearing Loss and Rehabilitation (38 papers) and Noise Effects and Management (18 papers). Arne Leijon collaborates with scholars based in Sweden, China and Germany. Arne Leijon's co-authors include Zhanyu Ma, Nasser Mohammadiha, Paris Smaragdis, Jalil Taghia, Jun Guo, Peter Händel, Ghazaleh Panahandeh, Anders Ringdahl, Zheng‐Hua Tan and Honggang Zhang and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, The Journal of the Acoustical Society of America and Pattern Recognition.

In The Last Decade

Arne Leijon

84 papers receiving 1.8k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Supervised and Unsupervised Speech Enhancement Using Nonnegative Matrix Factorization

2013 298 citations Nasser Mohammadiha, Arne Leijon et al. profile →

Peers

Arne Leijon

SP

AI

CN

CM

CVPR

Andrzej Czyżewski Poland

Yi Hu United States

Ahmet Alkan Türkiye

Juan Ignacio Godino-Llorente Spain

Noboru Ohnishi Japan

G. Castellanos-Domínguez Colombia

Mohammed Yeasin United States

Tomohiro Nakatani Japan

James M. Coughlan United States

Cüneyt Güzelіș Türkiye

Andrzej Czyżewski Poland

Arne Leijon

615 ×0.7

SP

287 ×0.5

AI

194 ×0.4

CN

73 ×0.2

CM

447 ×1.4

CVPR

Citations per year, relative to Arne Leijon Arne Leijon (= 1×) peers Andrzej Czyżewski

Countries citing papers authored by Arne Leijon

Since Specialization

Citations

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

Fields of papers citing papers by Arne Leijon

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Arne Leijon

This figure shows the co-authorship network connecting the top 25 collaborators of Arne Leijon. A scholar is included among the top collaborators of Arne Leijon 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 Arne Leijon. Arne Leijon is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Leijon, Arne, et al.. (2023). Bayesian analysis of Ecological Momentary Assessment (EMA) data collected in adults before and after hearing rehabilitation. Frontiers in Digital Health. 5. 1100705–1100705. 3 indexed citations

2.

Taghia, Jalil & Arne Leijon. (2015). Variational Inference for Watson Mixture Model. IEEE Transactions on Pattern Analysis and Machine Intelligence. 38(9). 1886–1900. 16 indexed citations

3.

Berninger, Erik, Åke Olofsson, & Arne Leijon. (2014). Analysis of Click-Evoked Auditory Brainstem Responses Using Time Domain Cross-Correlations Between Interleaved Responses. Ear and Hearing. 35(3). 318–329. 17 indexed citations

4.

Mohammadiha, Nasser, W. Bastiaan Kleijn, & Arne Leijon. (2013). Gamma hidden Markov model as a probabilistic Nonnegative Matrix Factorization. KTH Publication Database DiVA (KTH Royal Institute of Technology). 6811626. 2 indexed citations

5.

Ma, Zhanyu & Arne Leijon. (2010). Human skin color detection in RGB space with Bayesian estimation of beta mixture models. European Signal Processing Conference. 1204–1208. 23 indexed citations

6.

Ma, Zhanyu & Arne Leijon. (2010). Expectation propagation for estimating the parameters of the beta distribution. 2082–2085. 12 indexed citations

7.

Luts, Heleen, Jan Wouters, Norbert Dillier, et al.. (2008). Evaluation of signal enhancement algorithms for hearing instruments. Pure Amsterdam UMC. 1–5. 17 indexed citations

8.

Lutman, Mark E., et al.. (2005). Methodology for quantifying perceptual effects from noise suppression systems. International Journal of Audiology. 44(12). 721–732. 11 indexed citations

9.

Smeds, Karolina, Gitte Keidser, Harvey Dillon, et al.. (2005). Preferred overall loudness. I: Sound field presentation in the laboratory. International Journal of Audiology. 45(1). 2–11. 20 indexed citations

10.

Smeds, Karolina, Gitte Keidser, Harvey Dillon, et al.. (2005). Preferred overall loudness. II: Listening through hearing aids in field and laboratory tests. International Journal of Audiology. 45(1). 12–25. 38 indexed citations

11.

Leijon, Arne, et al.. (2004). An efficient robust sound classification algorithm for hearing aids. The Journal of the Acoustical Society of America. 115(6). 3033–3041. 41 indexed citations

12.

Leijon, Arne, et al.. (2004). Speech Recognition in Hearing Aids. EURASIP Journal on Wireless Communications and Networking. 1 indexed citations

13.

Smeds, Karolina & Arne Leijon. (2001). Threshold-based fitting methods for non-linear (WDRC) hearing instruments - comparison of acoustic characteristics. Scandinavian Audiology. 30(4). 213–222. 7 indexed citations

14.

Magnusson, Lennart, et al.. (2001). Predicted and Measured Speech Recognition Performance in Noise with Linear Amplification. Ear and Hearing. 22(1). 46–57. 17 indexed citations

15.

Leijon, Arne. (1992). Quantization Error in Clinical Pure-Tone Audiometry. Scandinavian Audiology. 21(2). 103–108. 10 indexed citations

16.

Leijon, Arne. (1991). Hearing Aid Gain for Loudness-Density Normalization in Cochlear Hearing Losses with Impaired Frequency Resolution. Ear and Hearing. 12(4). 242–250. 8 indexed citations

17.

Leijon, Arne, et al.. (1991). Sound Quality and Speech Reception for Prescribed Hearing Aid Frequency Responses. Ear and Hearing. 12(4). 251–260. 15 indexed citations

18.

Leijon, Arne, et al.. (1990). Preferred Hearing Aid Gain in Everyday Use after Prescriptive Fitting*. Ear and Hearing. 11(4). 299–305. 19 indexed citations

19.

Ringdahl, Anders, et al.. (1984). Hearing Aid Prescription Rules Using Insertion Gain Measurements. Scandinavian Audiology. 13(4). 211–218. 2 indexed citations

20.

Leijon, Arne, et al.. (1983). Audiometric Earphone Discomfort Level and Hearing Aid Saturation Sound Pressure Level for a 90 Decibel Input Signal (SSPL90) as Measured in the Human Ear Canal. Ear and Hearing. 4(4). 185–189. 7 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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