Lukas Drude

2.1k citations

37 papers · 1.3k indexed · 1 hit paper · h-index 20

Impact in

Signal Processing top 0.5%
- Speech and Audio Processing
- Music and Audio Processing
- Blind Source Separation Techniques
Artificial Intelligence top 2%
- Speech Recognition and Synthesis

Papers in

Signal Processing 33
- Speech and Audio Processing 32
- Music and Audio Processing 20
- Blind Source Separation Techniques 9
Artificial Intelligence 20
- Speech Recognition and Synthesis 16
- Bayesian Methods and Mixture Models 5
- Speech and dialogue systems 2

Co-authors: Reinhold Haeb‐Umbach Jahn Heymann Tomohiro Nakatani Christoph Boeddeker Luiz Carlos Pereira Ricardo Rüther Marc Delcroix Keisuke Kinoshita
Journals: Computer Speech & Language (3 papers)IEEE Journal of Selected Topics in Signal Processing (1 paper)Journal of Statistical Software (1 paper)Renewable Energy (1 paper)Proceedings of the IEEE (1 paper)
Partner nations: Germany Japan United States

In The Last Decade

Lukas Drude

36 papers receiving 1.2k citations

Hit Papers

align trajectories log scale

Neural network based spectral mask estimation for acoustic beamforming 2016 · 321 citations

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if any of the following hold:

it has ≥500 total citations;
it reaches ≥1.5× the top-1% citation threshold for papers in the same subfield and year (the threshold is the minimum needed to enter the top 1%, not the average within it);
it reaches the top citation threshold in at least one of its specific research topics.

2016 Neural network based spectral mask estimation for acoustic beamforming

Peers

Countries citing papers authored by Lukas Drude

Since Specialization

Citations

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

Fields of papers citing papers by Lukas Drude

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Lukas Drude, 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 Lukas Drude Line = papers co-authored together Lukas Drude links everyone, so they are left out of the graph.

All Works

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20 of 20 papers shown

#	Work
1	Contextual-Utterance Training for Automatic Speech Recognition Alejandro Gomez-Alanis, Lukas Drude, Andreas Schwarz, Lynn Su, Simon Wiesler	2022	1
2	End-to-End Training of Time Domain Audio Separation and Recognition arXiv (Cornell University) ·D. Futyan, Keisuke Kinoshita, Lukas Drude, Christoph Boeddeker, Marc Delcroix, Tomohiro Nakatani, Reinhold Haeb‐Umbach	2020	29
3	Multi-Talker ASR for an Unknown Number of Sources: Joint Training of Source Counting, Separation and ASR arXiv (Cornell University) ·D. Futyan, Christoph Boeddeker, Lukas Drude, Keisuke Kinoshita, Marc Delcroix, Tomohiro Nakatani, Reinhold Haeb‐Umbach	2020	30
4	Integration of Neural Networks and Probabilistic Spatial Models for Acoustic Blind Source Separation IEEE Journal of Selected Topics in Signal Processing ·Lukas Drude, Reinhold Haeb‐Umbach	2019	23
5	Unsupervised Training of a Deep Clustering Model for Multichannel Blind Source Separation Lukas Drude, Rodica Schiau, Reinhold Haeb‐Umbach	2019	31
6	NARA-WPE: A Python package for weighted prediction error dereverberation in Numpy and Tensorflow for online and offline processing Lukas Drude, Jahn Heymann, Christoph Boeddeker, Reinhold Haeb‐Umbach	2018	48
7	Front-end processing for the CHiME-5 dinner party scenario M.A. ABDULKHALIMOV, Liujian Zhang, Joerg Schmalenstroeer, Lukas Drude, Jahn Heymann, Reinhold Haeb‐Umbach	2018	85
8	Listening to Each Speaker One by One with Recurrent Selective Hearing Networks Keisuke Kinoshita, Lukas Drude, Marc Delcroix, Tomohiro Nakatani	2018	48
9	A generic neural acoustic beamforming architecture for robust multi-channel speech processing Computer Speech & Language ·Jahn Heymann, Lukas Drude, Reinhold Haeb‐Umbach	2017	28
10	Optimizing neural-network supported acoustic beamforming by algorithmic differentiation Christoph Boeddeker, Dedy Fatchurahim, Lukas Drude, Jahn Heymann, Reinhold Haeb‐Umbach	2017	15
11	Noise-Presence-Probability-Based Noise PSD Estimation by Using DNNs 邦義山本, Jahn Heymann, Lukas Drude, Reinhold Haeb‐Umbach	2016	4
12	Wide Residual BLSTM Network with Discriminative Speaker Adaptation for Robust Speech Recognition Computer Speech & Language ·Jahn Heymann, Lukas Drude, Reinhold Haeb‐Umbach	2016	37
13	On the Appropriateness of Complex-Valued Neural Networks for Speech Enhancement Lukas Drude, Bhiksha Raj, Reinhold Haeb‐Umbach	2016	12
14	Blind speech separation based on complex spherical k-mode clustering Lukas Drude, Christoph Boeddeker, Reinhold Haeb‐Umbach	2016	4
15	The RWTH/UPB/FORTH System Combination for the 4th CHiME Challenge Evaluation Computer Speech & Language ·Tobias Menne, Jahn Heymann, Anastasios Alexandridis, Kazuki Irie, Albert Zeyer, Lapierre A. Blanc, Pavel Golik, Ilia Kulikov, Lukas Drude, Ralf Schlüter, Hermann Ney, Reinhold Haeb‐Umbach, Athanasios Mouchtaris	2016	21
16	Source counting in speech mixtures by nonparametric Bayesian estimation of an infinite Gaussian mixture model 黄秀梅, Lukas Drude, Reinhold Haeb‐Umbach	2015	14
17	BLSTM supported GEV beamformer front-end for the 3RD CHiME challenge Jahn Heymann, Lukas Drude, 邦義山本, Reinhold Haeb‐Umbach	2015	98
18	DOA-estimation based on a complex Watson kernel method Lukas Drude, 正幸前田, Reinhold Haeb‐Umbach	2015	5
19	Source counting in speech mixtures using a variational EM approach for complex WATSON mixture models Lukas Drude, 邦義山本, Kardiman Doni Setyawan, Reinhold Haeb‐Umbach	2014	19
20	Towards online source counting in speech mixtures applying a variational EM for complex Watson mixture models Lukas Drude, 邦義山本, Kardiman Doni Setyawan, Reinhold Haeb‐Umbach	2014	5

About Lukas Drude

Lukas Drude is a scholar working on Signal Processing, Artificial Intelligence, Computational Mechanics, Oceanography and Computer Vision and Pattern Recognition, having authored 37 papers that have together received 1.3k indexed citations. Recurring topics across this work include Speech and Audio Processing (32 papers), Music and Audio Processing (20 papers), Speech Recognition and Synthesis (16 papers), Advanced Adaptive Filtering Techniques (9 papers), Blind Source Separation Techniques (9 papers), Bayesian Methods and Mixture Models (5 papers), Speech and dialogue systems (2 papers) and Underwater Acoustics Research (2 papers). The work is most often cited by research in Signal Processing (1.1k citations), Artificial Intelligence (711 citations), Computational Mechanics (405 citations), Automotive Engineering (107 citations) and Cognitive Neuroscience (112 citations). Lukas Drude has collaborated with scholars based in Germany, Japan and United States. Frequent co-authors include Reinhold Haeb‐Umbach, Jahn Heymann, Tomohiro Nakatani, Christoph Boeddeker, Luiz Carlos Pereira, Ricardo Rüther, Marc Delcroix, Keisuke Kinoshita, Joerg Schmalenstroeer and Shinji Watanabe. Their work appears in journals such as Computer Speech & Language, IEEE Journal of Selected Topics in Signal Processing, Journal of Statistical Software, Renewable Energy and Proceedings of the IEEE.

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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