F.J. Cañadas-Quesada

701 total citations
50 papers, 462 citations indexed

About

F.J. Cañadas-Quesada is a scholar working on Signal Processing, Pulmonary and Respiratory Medicine and Computer Vision and Pattern Recognition. According to data from OpenAlex, F.J. Cañadas-Quesada has authored 50 papers receiving a total of 462 indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Signal Processing, 14 papers in Pulmonary and Respiratory Medicine and 12 papers in Computer Vision and Pattern Recognition. Recurrent topics in F.J. Cañadas-Quesada's work include Music and Audio Processing (32 papers), Speech and Audio Processing (29 papers) and Phonocardiography and Auscultation Techniques (14 papers). F.J. Cañadas-Quesada is often cited by papers focused on Music and Audio Processing (32 papers), Speech and Audio Processing (29 papers) and Phonocardiography and Auscultation Techniques (14 papers). F.J. Cañadas-Quesada collaborates with scholars based in Spain, Finland and Poland. F.J. Cañadas-Quesada's co-authors include N. Ruiz-Reyes, P. Vera‐Candeas, Julio J. Carabias-Orti, Sebastián García Galán, J. Rey, Julián Martínez, Tuomas Virtanen, Luis Elvira, F. Montero de Espinosa and Elías F. Combarro and has published in prestigious journals such as Construction and Building Materials, Expert Systems with Applications and IEEE Access.

In The Last Decade

F.J. Cañadas-Quesada

46 papers receiving 448 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
F.J. Cañadas-Quesada Spain 14 292 135 111 40 39 50 462
Shiwen Deng China 12 237 0.8× 351 2.6× 47 0.4× 5 0.1× 10 0.3× 42 594
Michael Pitz Germany 11 342 1.2× 20 0.1× 69 0.6× 11 0.3× 21 0.5× 19 495
Nicoletta Saulig Croatia 9 142 0.5× 13 0.1× 113 1.0× 6 0.1× 19 0.5× 38 319
Saïd Assous United Kingdom 9 40 0.1× 10 0.1× 62 0.6× 58 1.4× 55 1.4× 27 301
Petr Koudelka Czechia 14 50 0.2× 31 0.2× 18 0.2× 15 0.4× 11 0.3× 70 491
Julio J. Carabias-Orti Spain 14 367 1.3× 115 0.9× 113 1.0× 1 0.0× 11 0.3× 46 425
C. Papaodysseus Greece 12 100 0.3× 19 0.1× 360 3.2× 5 0.1× 5 0.1× 57 486
Robin L. Murray United States 5 71 0.2× 42 0.3× 41 0.4× 18 0.5× 16 0.4× 13 399
Arman Kheirati Roonizi Iran 10 45 0.2× 19 0.1× 58 0.5× 5 0.1× 6 0.2× 27 253
Dinko Oletić Croatia 10 62 0.2× 85 0.6× 24 0.2× 11 0.3× 18 0.5× 26 327

Countries citing papers authored by F.J. Cañadas-Quesada

Since Specialization
Citations

This map shows the geographic impact of F.J. Cañadas-Quesada'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 F.J. Cañadas-Quesada with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites F.J. Cañadas-Quesada more than expected).

Fields of papers citing papers by F.J. Cañadas-Quesada

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by F.J. Cañadas-Quesada. 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 F.J. Cañadas-Quesada. The network helps show where F.J. Cañadas-Quesada may publish in the future.

Co-authorship network of co-authors of F.J. Cañadas-Quesada

This figure shows the co-authorship network connecting the top 25 collaborators of F.J. Cañadas-Quesada. A scholar is included among the top collaborators of F.J. Cañadas-Quesada 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 F.J. Cañadas-Quesada. F.J. Cañadas-Quesada is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Galán, Sebastián García, et al.. (2024). Expert system against machine learning approaches as a virtual sensor for ventricular arrhythmia risk level estimation. Biomedical Signal Processing and Control. 102. 107255–107255.
2.
Cañadas-Quesada, F.J., et al.. (2024). Noise-tolerant NMF-based parallel algorithm for respiratory rate estimation. The Journal of Supercomputing. 80(19). 26922–26941. 1 indexed citations
3.
Cañadas-Quesada, F.J., et al.. (2023). Cochleogram-based adventitious sounds classification using convolutional neural networks. Biomedical Signal Processing and Control. 82. 104555–104555. 19 indexed citations
4.
Carabias-Orti, Julio J., et al.. (2023). Automatic Robust Crackle Detection and Localization Approach Using AR-Based Spectral Estimation and Support Vector Machine. Applied Sciences. 13(19). 10683–10683. 2 indexed citations
5.
Cañadas-Quesada, F.J., N. Ruiz-Reyes, P. Vera‐Candeas, et al.. (2023). Detection of valvular heart diseases combining orthogonal non-negative matrix factorization and convolutional neural networks in PCG signals. Journal of Biomedical Informatics. 145. 104475–104475. 5 indexed citations
6.
Cañadas-Quesada, F.J., et al.. (2021). Monophonic and Polyphonic Wheezing Classification Based on Constrained Low-Rank Non-Negative Matrix Factorization. Sensors. 21(5). 1661–1661. 9 indexed citations
7.
Cañadas-Quesada, F.J., et al.. (2021). Parallel source separation system for heart and lung sounds. The Journal of Supercomputing. 77(8). 8135–8150. 6 indexed citations
8.
Cañadas-Quesada, F.J., et al.. (2020). Wheezing Sound Separation Based on Informed Inter-Segment Non-Negative Matrix Partial Co-Factorization. Sensors. 20(9). 2679–2679. 10 indexed citations
9.
Cañadas-Quesada, F.J., et al.. (2019). A constrained tonal semi-supervised non-negative matrix factorization to classify presence/absence of wheezing in respiratory sounds. Applied Acoustics. 161. 107188–107188. 13 indexed citations
10.
Cañadas-Quesada, F.J., et al.. (2016). Constrained non-negative matrix factorization for score-informed piano music restoration. Digital Signal Processing. 50. 240–257. 15 indexed citations
11.
Vera‐Candeas, P., et al.. (2015). Compositional model for speech denoising based on source/filter speech representation and smoothness/sparseness noise constraints. Speech Communication. 78. 84–99. 3 indexed citations
12.
Cañadas-Quesada, F.J., et al.. (2015). Difficulties Using Passive Haptic Augmentation in the Interaction within a Virtual Environment.
13.
Elvira, Luis, et al.. (2015). Concentration measurement of yeast suspensions using high frequency ultrasound backscattering. Ultrasonics. 64. 151–161. 30 indexed citations
14.
Carabias-Orti, Julio J., et al.. (2015). An Audio To Score Alignment Framework Using Spectral Factorization And Dynamic Time Warping.. Zenodo (CERN European Organization for Nuclear Research). 742–748. 18 indexed citations
15.
Carabias-Orti, Julio J., et al.. (2009). Estimating Instrument Spectral Envelopes for Polyphonic Music Transcription in a Music Scene-Adaptive Approach. Journal of the Audio Engineering Society.
16.
Ruiz-Reyes, N., et al.. (2009). Comparing open-source e-learning platforms from adaptivity point of view. 1–6. 17 indexed citations
17.
Cañadas-Quesada, F.J., et al.. (2009). A Joint Approach to Extract Multiple Fundamental Frequency in Polyphonic Signals Minimizing Gaussian Spectral Distance. Journal of the Audio Engineering Society. 1 indexed citations
18.
Cañadas-Quesada, F.J., et al.. (2008). Polyphonic Piano Transcription Based on Spectral Separation. Journal of the Audio Engineering Society.
19.
Alexandre, Enrique, F.J. Cañadas-Quesada, Manuel Rosa-Zurera, N. Ruiz-Reyes, & P. Vera‐Candeas. (2008). Musical-Inspired Features for Automatic Sound Classification in Digital Hearing Aids. Journal of the Audio Engineering Society. 2 indexed citations
20.
Cañadas-Quesada, F.J., et al.. (2006). Improvement of Perceived Stiffness Using Auditory Stimuli in Haptic Virtual Reality. 462–465. 6 indexed citations

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.

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