Joonas Nikunen
- Signal Processing top 1%
- Computational Mechanics top 10%
- Artificial Intelligence top 10%
- Cognitive Neuroscience
- Computer Vision and Pattern Recognition top 10%
- Co-authors
- Tuomas VirtanenArchontis PolitisSharath AdavannePasi PertiläJulio J. Carabias-OrtiP. Vera‐CandeasAleksandr Diment
- Topics
- Speech and Audio Processing (16 papers)Advanced Adaptive Filtering Techniques (14 papers)Blind Source Separation Techniques (6 papers)
In The Last Decade
Joonas Nikunen
16 papers receiving 505 citations
Hit Papers
Peers
Comparison fields: 5 of 44
- Signal Processing 488
- Computational Mechanics 140
- Artificial Intelligence 106
- Cognitive Neuroscience 86
- Computer Vision and Pattern Recognition 79
Countries citing papers authored by Joonas Nikunen
This map shows the geographic impact of Joonas Nikunen'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 Joonas Nikunen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Joonas Nikunen more than expected).
Fields of papers citing papers by Joonas Nikunen
This network shows the impact of papers produced by Joonas Nikunen. 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 Joonas Nikunen. The network helps show where Joonas Nikunen may publish in the future.
Co-authorship network of co-authors of Joonas Nikunen
This figure shows the co-authorship network connecting the top 25 collaborators of Joonas Nikunen. A scholar is included among the top collaborators of Joonas Nikunen 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 Joonas Nikunen. Joonas Nikunen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 20 | |
| 3 | 8 | |
| 4 | 11 | |
| 5 | Sound Event Localization and Detection of Overlapping Sources Using Convolutional Recurrent Neural Networksbreakdown → | 328 |
| 6 | 3 | |
| 7 | 3 | |
| 8 | 24 | |
| 9 | 69 | |
| 10 | 11 | |
| 11 | 18 | |
| 12 | Multichannel Audio Upmixing by Time-Frequency Filtering Using Non-Negative Tensor Factorization | 3 |
| 13 | 2 | |
| 14 | 8 | |
| 15 | Object-Based Audio Coding Using Non-Negative Matrix Factorization for the Spectrogram Representation | 10 |
| 16 | 7 |
About Joonas Nikunen
Joonas Nikunen is a scholar working on Signal Processing, Computational Mechanics and Cognitive Neuroscience, having authored 16 papers that have together received 528 indexed citations. Recurring topics across this work include Speech and Audio Processing (16 papers), Advanced Adaptive Filtering Techniques (14 papers) and Blind Source Separation Techniques (6 papers). The work is most often cited by research in Signal Processing (488 citations), Developmental Biology (31 citations) and Computational Mathematics (6 citations). Joonas Nikunen has collaborated with scholars based in Finland, Spain and Denmark. Frequent co-authors include Tuomas Virtanen, Archontis Politis, Sharath Adavanne, Pasi Pertilä, Julio J. Carabias-Orti, P. Vera‐Candeas and Aleksandr Diment. Their work appears in journals such as IEEE Journal of Selected Topics in Signal Processing, Speech Communication and IEEE/ACM Transactions on Audio Speech and Language Processing.
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.