Sebastian Streich
- Signal Processing top 5%
- Computer Vision and Pattern Recognition top 5%
- Cognitive Neuroscience
- Artificial Intelligence
- Music top 10%
- Co-authors
- Emília GómezGraham E. PolinerDaniel P. W. EllisAndreas F. EhmannPerfecto HerreraFabien GouyonPedro CanoXavier Serra
- Topics
- Music and Audio Processing (9 papers)Music Technology and Sound Studies (7 papers)Speech and Audio Processing (7 papers)
In The Last Decade
Sebastian Streich
11 papers receiving 213 citations
Peers
Comparison fields: 5 of 26
- Signal Processing 235
- Computer Vision and Pattern Recognition 190
- Cognitive Neuroscience 33
- Artificial Intelligence 19
- Music 13
Countries citing papers authored by Sebastian Streich
This map shows the geographic impact of Sebastian Streich'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 Sebastian Streich with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sebastian Streich more than expected).
Fields of papers citing papers by Sebastian Streich
This network shows the impact of papers produced by Sebastian Streich. 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 Sebastian Streich. The network helps show where Sebastian Streich may publish in the future.
Co-authorship network of co-authors of Sebastian Streich
This figure shows the co-authorship network connecting the top 25 collaborators of Sebastian Streich. A scholar is included among the top collaborators of Sebastian Streich 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 Sebastian Streich. Sebastian Streich 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 | A MUSIC LOOP EXPLORER SYSTEM | 5 |
| 3 | 4 | |
| 4 | 129 | |
| 5 | 11 | |
| 6 | ISMIR 2004 Audio Description Contest | 48 |
| 7 | A Quantitative Comparison of Different Approaches for Melody Extraction from Polyphonic Audio Recordings | 9 |
| 8 | Automatic Extraction of Musical Structure Using Pitch Class Distribution Features | 7 |
| 9 | Detrended Fluctuation Analysis of Music Signals: Danceability Estimation and Further Semantic Characterization | 17 |
| 10 | Towards Describing Perceived Complexity of Songs: Computational Methods and Implementation | 2 |
| 11 | Perceptual Optimization of the Frequency Selective Switch in Scalable Audio Coding | 1 |
| 12 | 9 |
About Sebastian Streich
Sebastian Streich is a scholar working on Signal Processing, Computer Vision and Pattern Recognition and Language and Linguistics, having authored 12 papers that have together received 242 indexed citations. Recurring topics across this work include Music and Audio Processing (9 papers), Music Technology and Sound Studies (7 papers) and Speech and Audio Processing (7 papers). The work is most often cited by research in Signal Processing (235 citations), Computer Vision and Pattern Recognition (190 citations) and Music (13 citations). Sebastian Streich has collaborated with scholars based in Spain, Germany and Japan. Frequent co-authors include Emília Gómez, Graham E. Poliner, Daniel P. W. Ellis, Andreas F. Ehmann, Perfecto Herrera, Fabien Gouyon, Pedro Cano, Xavier Serra, Nicolas Wack and Ye Wang. Their work appears in journals such as IEEE Transactions on Audio Speech and Language Processing, Academic Radiology and Journal of the Audio Engineering Society.
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.