Renato Panda
- Signal Processing top 2%
- Cognitive Neuroscience top 10%
- Computer Vision and Pattern Recognition top 5%
- Experimental and Cognitive Psychology top 10%
- Artificial Intelligence
- Co-authors
- Rui Pedro PaivaRicardo MalheiroBruno RochaAntônio Pedro Novaes de OliveiraLuís OliveiraGabriel PiresTelmo PereiraAna Lopes
- Topics
- Music and Audio Processing (18 papers)Speech and Audio Processing (13 papers)Music Technology and Sound Studies (7 papers)
In The Last Decade
Renato Panda
23 papers receiving 375 citations
Peers
Comparison fields: 5 of 49
- Signal Processing 291
- Cognitive Neuroscience 168
- Computer Vision and Pattern Recognition 145
- Experimental and Cognitive Psychology 87
- Artificial Intelligence 62
Countries citing papers authored by Renato Panda
This map shows the geographic impact of Renato Panda'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 Renato Panda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Renato Panda more than expected).
Fields of papers citing papers by Renato Panda
This network shows the impact of papers produced by Renato Panda. 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 Renato Panda. The network helps show where Renato Panda may publish in the future.
Co-authorship network of co-authors of Renato Panda
This figure shows the co-authorship network connecting the top 25 collaborators of Renato Panda. A scholar is included among the top collaborators of Renato Panda 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 Renato Panda. Renato Panda is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 3 | |
| 3 | 6 | |
| 4 | 1 | |
| 5 | 6 | |
| 6 | 13 | |
| 7 | 107 | |
| 8 | 7 | |
| 9 | Bi-modal music emotion recognition: Novel lyrical features and dataset | 7 |
| 10 | 8 | |
| 11 | 49 | |
| 12 | 23 | |
| 13 | 7 | |
| 14 | 7 | |
| 15 | Music Emotion Recognition from Lyrics: A Comparative Study | 7 |
| 16 | Multi-Modal Music Emotion Recognition: A New Dataset, Methodology and Comparative Analysis | 24 |
| 17 | Music Emotion Classification: Analysis of a Classifier Ensemble Approach | 3 |
| 18 | Using Support Vector Machines for Automatic Mood Tracking in Audio Music | 19 |
| 19 | 2 | |
| 20 | MOODetector: A Prototype Software Tool for Mood-based Playlist Generation | 5 |
About Renato Panda
Renato Panda is a scholar working on Signal Processing, Computer Vision and Pattern Recognition and Cognitive Neuroscience, having authored 23 papers that have together received 396 indexed citations. Recurring topics across this work include Music and Audio Processing (18 papers), Speech and Audio Processing (13 papers) and Music Technology and Sound Studies (7 papers). The work is most often cited by research in Signal Processing (291 citations), Cognitive Neuroscience (168 citations) and Computer Vision and Pattern Recognition (145 citations). Renato Panda has collaborated with scholars based in Portugal and Brazil. Frequent co-authors include Rui Pedro Paiva, Ricardo Malheiro, Bruno Rocha, Antônio Pedro Novaes de Oliveira, Luís Oliveira, Gabriel Pires, Telmo Pereira, Ana Lopes, Gabriel G. Martins and César F. Lima. Their work appears in journals such as Sensors, IEEE Transactions on Affective Computing 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.