Christoph Boeddeker
- Signal Processing top 1%
- Speech and Audio Processing 31
- Music and Audio Processing 22
- Blind Source Separation Techniques 2
- Artificial Intelligence top 5%
- Speech Recognition and Synthesis 27
- Speech and dialogue systems 3
- Natural Language Processing Techniques 3
- Computational Mechanics top 10%
- Advanced Adaptive Filtering Techniques 4
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- Advanced Data Compression Techniques 2
- Co-authors
- Reinhold Haeb‐UmbachLukas DrudeJahn HeymannMarc DelcroixKeisuke KinoshitaTomohiro NakataniTakuya YoshiokaShinji Watanabe
- Journals
- IEEE Signal Processing Magazine (1 paper)IEEE/ACM Transactions on Audio Speech and Language Processing (3 papers)arXiv (Cornell University) (5 papers)
- Partner nations
- GermanyJapanUnited States
In The Last Decade
Christoph Boeddeker
34 papers receiving 506 citations
Peers
Comparison fields: 5 of 31
- Signal Processing 502
- Artificial Intelligence 393
- Computational Mechanics 124
- Cognitive Neuroscience 33
- Oceanography 9
Countries citing papers authored by Christoph Boeddeker
This map shows the geographic impact of Christoph Boeddeker'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 Christoph Boeddeker with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Christoph Boeddeker more than expected).
Fields of papers citing papers by Christoph Boeddeker
This network shows the impact of papers produced by Christoph Boeddeker. 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 Christoph Boeddeker. The network helps show where Christoph Boeddeker may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Christoph Boeddeker, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2024 | 3 | |
| 3 | 2024 | 14 | |
| 4 | 2024 | 1 | |
| 5 | 2024 | 0 | |
| 6 | 2024 | 1 | |
| 7 | 2024 | 8 | |
| 8 | 2023 | 10 | |
| 9 | 2023 | 1 | |
| 10 | 2023 | 2 | |
| 11 | 2022 | 13 | |
| 12 | 2022 | 5 | |
| 13 | 2022 | 15 | |
| 14 | 2021 | 23 | |
| 15 | 2021 | 18 | |
| 16 | 2020 | 53 | |
| 17 | 2020 | 29 | |
| 18 | 2020 | 2 | |
| 19 | NARA-WPE: A Python package for weighted prediction error dereverberation in Numpy and Tensorflow for online and offline processing | 2018 | 48 |
| 20 | 2017 | 15 |
About Christoph Boeddeker
Christoph Boeddeker is a scholar working on Signal Processing, Artificial Intelligence and Computational Mechanics, having authored 36 papers that have together received 537 indexed citations. Recurring topics across this work include Speech and Audio Processing (31 papers), Speech Recognition and Synthesis (27 papers), Music and Audio Processing (22 papers), Advanced Adaptive Filtering Techniques (4 papers), Speech and dialogue systems (3 papers), Natural Language Processing Techniques (3 papers), Blind Source Separation Techniques (2 papers) and Advanced Data Compression Techniques (2 papers). The work is most often cited by research in Signal Processing (502 citations), Artificial Intelligence (393 citations) and Computational Mechanics (124 citations). Christoph Boeddeker has collaborated with scholars based in Germany, Japan and United States. Frequent co-authors include Reinhold Haeb‐Umbach, Lukas Drude, Jahn Heymann, Marc Delcroix, Keisuke Kinoshita, Tomohiro Nakatani, Takuya Yoshioka, Shinji Watanabe, Hakan Erdoğan and Wangyou Zhang. Their work appears in journals such as IEEE Signal Processing Magazine, IEEE/ACM Transactions on Audio Speech and Language Processing and arXiv (Cornell University).
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.