Hannah Muckenhirn
- Signal Processing top 2%
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition
- Experimental and Cognitive Psychology
- Computational Mechanics
- Co-authors
- Mathew Magimai.-DossSébastien MarcelZelin WuRif A. SaurousQuan WangJohn R. HersheyIgnacio López MorenoKevin Wilson
- Topics
- Speech and Audio Processing (9 papers)Speech Recognition and Synthesis (9 papers)Music and Audio Processing (7 papers)
- Journals
- IEEE/ACM Transactions on Audio Speech and Language ProcessingInfoscience (Ecole Polytechnique Fédérale de Lausanne)Interspeech 2022
- Partner nations
- SwitzerlandUnited StatesIndia
In The Last Decade
Hannah Muckenhirn
10 papers receiving 463 citations
Peers
Comparison fields: 5 of 38
- Signal Processing 416
- Artificial Intelligence 405
- Computer Vision and Pattern Recognition 36
- Experimental and Cognitive Psychology 26
- Computational Mechanics 24
Countries citing papers authored by Hannah Muckenhirn
This map shows the geographic impact of Hannah Muckenhirn'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 Hannah Muckenhirn with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hannah Muckenhirn more than expected).
Fields of papers citing papers by Hannah Muckenhirn
This network shows the impact of papers produced by Hannah Muckenhirn. 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 Hannah Muckenhirn. The network helps show where Hannah Muckenhirn may publish in the future.
Co-authorship network of co-authors of Hannah Muckenhirn
This figure shows the co-authorship network connecting the top 25 collaborators of Hannah Muckenhirn. A scholar is included among the top collaborators of Hannah Muckenhirn 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 Hannah Muckenhirn. Hannah Muckenhirn is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 217 | |
| 3 | 20 | |
| 4 | Gradient-based spectral visualization of CNNs using raw waveforms | 4 |
| 5 | 7 | |
| 6 | 29 | |
| 7 | 81 | |
| 8 | 33 | |
| 9 | 28 | |
| 10 | 14 | |
| 11 | 54 |
About Hannah Muckenhirn
Hannah Muckenhirn is a scholar working on Signal Processing, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 11 papers that have together received 487 indexed citations. Recurring topics across this work include Speech and Audio Processing (9 papers), Speech Recognition and Synthesis (9 papers) and Music and Audio Processing (7 papers). The work is most often cited by research in Signal Processing (416 citations), Artificial Intelligence (405 citations) and Experimental and Cognitive Psychology (26 citations). Hannah Muckenhirn has collaborated with scholars based in Switzerland, United States and India. Frequent co-authors include Mathew Magimai.-Doss, Sébastien Marcel, Zelin Wu, Rif A. Saurous, Quan Wang, John R. Hershey, Ignacio López Moreno, Kevin Wilson, Prashant Sridhar and Jia Ye. Their work appears in journals such as IEEE/ACM Transactions on Audio Speech and Language Processing, Infoscience (Ecole Polytechnique Fédérale de Lausanne) and Interspeech 2022.
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.