Ricardo Malheiro
- Signal Processing top 5%
- Cognitive Neuroscience top 10%
- Computer Vision and Pattern Recognition top 10%
- Experimental and Cognitive Psychology top 10%
- Artificial Intelligence
- Co-authors
- Rui Pedro PaivaRenato PandaBruno RochaAntônio Pedro Novaes de OliveiraAntónio José MendesAmílcar CardosoHugo Gonçalo Oliveira
- Topics
- Music and Audio Processing (12 papers)Speech and Audio Processing (10 papers)Music Technology and Sound Studies (5 papers)
- Journals
- SensorsIEEE Transactions on Affective ComputingEstudo Geral (Universidade de Coimbra)
In The Last Decade
Ricardo Malheiro
13 papers receiving 284 citations
Peers
Comparison fields: 5 of 36
- Signal Processing 228
- Cognitive Neuroscience 131
- Computer Vision and Pattern Recognition 113
- Experimental and Cognitive Psychology 78
- Artificial Intelligence 55
Countries citing papers authored by Ricardo Malheiro
This map shows the geographic impact of Ricardo Malheiro'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 Ricardo Malheiro with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ricardo Malheiro more than expected).
Fields of papers citing papers by Ricardo Malheiro
This network shows the impact of papers produced by Ricardo Malheiro. 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 Ricardo Malheiro. The network helps show where Ricardo Malheiro may publish in the future.
Co-authorship network of co-authors of Ricardo Malheiro
This figure shows the co-authorship network connecting the top 25 collaborators of Ricardo Malheiro. A scholar is included among the top collaborators of Ricardo Malheiro 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 Ricardo Malheiro. Ricardo Malheiro 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 | 6 | |
| 3 | 70 | |
| 4 | 13 | |
| 5 | 107 | |
| 6 | Bi-modal music emotion recognition: Novel lyrical features and dataset | 7 |
| 7 | 8 | |
| 8 | 2 | |
| 9 | 49 | |
| 10 | Music Emotion Recognition from Lyrics: A Comparative Study | 7 |
| 11 | Multi-Modal Music Emotion Recognition: A New Dataset, Methodology and Comparative Analysis | 24 |
| 12 | Umniversity virtual world platform for massive open online courses University Platform | 2 |
| 13 | A prototype for classification of classical music using neural networks | 4 |
About Ricardo Malheiro
Ricardo Malheiro is a scholar working on Signal Processing, Computer Vision and Pattern Recognition and Cognitive Neuroscience, having authored 13 papers that have together received 302 indexed citations. Recurring topics across this work include Music and Audio Processing (12 papers), Speech and Audio Processing (10 papers) and Music Technology and Sound Studies (5 papers). The work is most often cited by research in Signal Processing (228 citations), Cognitive Neuroscience (131 citations) and Experimental and Cognitive Psychology (78 citations). Ricardo Malheiro has collaborated with scholars based in Portugal and Brazil. Frequent co-authors include Rui Pedro Paiva, Renato Panda, Bruno Rocha, Antônio Pedro Novaes de Oliveira, António José Mendes, Amílcar Cardoso and Hugo Gonçalo Oliveira. Their work appears in journals such as Sensors, IEEE Transactions on Affective Computing and Estudo Geral (Universidade de Coimbra).
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.