Sulun S., Davies M. E. P. & Viana P. (2022). “Symbolic Music Generation Conditioned on Continuous-Valued Emotions”. IEEE Access, Vol. 10, pp. 44617-44626, DOI: 10.1109/TAFFC.2020.3032373.

Panda R. (2010). “Automatic Mood Tracking in Audio Music”. Master on Informatics Engineering, Department of Informatics Engineering, University of Coimbra, Portugal, July 2010.

Fernandes J. (2010). “Automatic Playlist Generation via Music Mood Analysis”. Master on Informatics Engineering, Department of Informatics Engineering, University of Coimbra, Portugal, September 2010.

Panda R., Malheiro R. & Paiva R. P. (2023). “Audio Features for Music Emotion Recognition: a Survey”. IEEE Transactions on Affective Computing, Vol. 14(1), pp. 68-88, 10.1109/TAFFC.2020.3032373.

Panda R., Malheiro R. & Paiva R. P. (2020). “Novel audio features for music emotion recognition”. IEEE Transactions on Affective Computing, Vol. 11(4), pp. 614-626. DOI: 10.1109/TAFFC.2018.2820691.

Malheiro R., Panda R., Gomes P. & Paiva R. P. (2018). “Emotionally-Relevant Features for Classification and Regression of Music Lyrics”. IEEE Transactions on Affective Computing, Vol. 9(2), pp. 240-254, doi:10.1109/TAFFC.2016.2598569.

Panda R., Rocha B., Paiva R. P. (2015). “Music Emotion Recognition with Standard and Melodic Audio Features”. Applied Artificial Intelligence, Vol. 29:4, pp. 313-334, Taylor & Francis.

Panda R., Redinho H., Gonçalves C., Malheiro R. & Paiva R. P. (2021). “How Does the Spotify API Compare to the Music Emotion Recognition State-of-the-Art?”. 18th Sound and Music Computing Conference – SMC 2021, June 29 – July 1 (virtual conference).

Panda R., Malheiro R., Paiva R. P. (2018). “Musical Texture and Expressivity Features for Music Emotion Recognition”. 19th International Society for Music Information Retrieval Conference – ISMIR 2018, Paris, France.

Malheiro R., Panda R., Gomes P. & Paiva R. P. (2016). “Classification and Regression of Music Lyrics: Emotionally-Significant Features”. 8th International Conference on Knowledge Discovery and Information Retrieval – KDIR'2016, Porto, Portugal.

Malheiro R., Oliveira H. G., Gomes P. & Paiva R. P. (2016). “Keyword-Based Approach for Lyrics Emotion Variation Detection”. 8th International Conference on Knowledge Discovery and Information Retrieval – KDIR'2016, Porto, Portugal.

Malheiro R., Panda R., Gomes P. & Paiva R. P. (2016). “Bi-Modal Music Emotion Recognition: Novel Lyrical Features and Dataset”. 9th International Workshop on Music and Machine Learning – MML'2016 – in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases – ECML/PKDD 2016, Riva del Garda, Italy.

Panda R., Malheiro R., Rocha B., Oliveira A. & Paiva R. P. (2013). “Multi-Modal Music Emotion Recognition: A New Dataset, Methodology and Comparative Analysis”. 10th International Symposium on Computer Music Multidisciplinary Research – CMMR’2013, Marseille, France.

Panda R., Rocha B., & Paiva R. P. (2013). “Dimensional Music Emotion Recognition: Combining Standard and Melodic Audio Features”. 10th International Symposium on Computer Music Multidisciplinary Research – CMMR’2013, Marseille, France.

Rocha B., Panda R. & Paiva R. P. (2013). “Music Emotion Recognition: The Importance of Melodic Features”. 6th International Workshop on Music and Machine Learning – MML’2013 – in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases – ECML/PKDD 2013, Prague, Czech Republic.

Malheiro R., Panda R., Gomes P. & Paiva R. P. (2013). “Music Emotion Recognition from Lyrics: A Comparative Study”. 6th International Workshop on Music and Machine Learning – MML’2013 – in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases – ECML/PKDD 2013, Prague, Czech Republic.

Panda R and Paiva R. P. (2012). “MIREX 2012: Mood Classifcation Task Submission”. Proceedings of the Music Information Retrieval Exchange – MIREX'2012.

Panda R. and Paiva R. P. (2012). “Music Emotion Classification: Dataset Acquisition and Comparative Analysis”. 15th International Conference on Digital Audio Effects – DAFx ’12, York, UK.

Panda R. and Paiva R. P. (2012). “Music Emotion Classification: Analysis of a Classifier Ensemble Approach”. 5th International Workshop on Machine Learning and Music – MML ’2012 (at the 29th International Conference on Machine Learning – ICML’2012), Edinburgh, Scotland, UK.

Cardoso L., Panda R and Paiva R. P. (2011). “MOODetector: A Prototype Software Tool for  Mood-based Playlist Generation”. Simpósio de Informática – INForum 2011, Coimbra, Portugal.

Panda R. and Paiva R. P. (2011). “Automatic Creation of Mood Playlists in the Thayer Plane: a Methodology and a Comparative Study”. 8th Sound and Music Computing Conference – SMC’2011, Padova, Italy.

Paiva R. P. (2013). “Music Data Mining: Automatic Emotion Recognition”. Invited Presentation. Ciclo de Conferencias en Ingeniería Informática, Universidad Central del Ecuador, Quito, Ecuador. October 25, 2013.

Paiva R. P. (2013). “MOODetector: Automatic Music Emotion Recognition”. Invited Presentation. Campus Party Quito 3, Quito, Equador. September 20, 2013.

Paiva R. P. (2012). “From Music Information Retrieval to Music Emotion Recognition”. Internal Presentation. Center for Informatics and Systems of the University of Coimbra, Portugal.

Paiva R. P. (2010). “MOODetector: A System for Mood-based Classification and Retrieval of Audio Music”. Invited Presentation. 1as. Jornadas de la Sociedad Ibérica de Tecnología Musical, Madrid, Spain. December 2010.

Paiva R. P. (2009). “Melody Detection in Polyphonic Audio: From Pitch Extraction to Note Identification”. Lambert Academic Publishing. ISBN 978-3838319704.

Paiva R. P. (2007). “Melody Detection in Polyphonic Audio”. PhD Thesis, Department of Informatics Engineering, University of Coimbra, Portugal, February 2007.

Paiva R. P., Mendes T. and Cardoso A. (2008). “From Pitches to Notes: Creation and Segmentation of Pitch Tracks for Melody Detection in Polyphonic Audio”. Journal of New Music Research, Vol. 37, No. 3, pp 185-205, Taylor and Francis.

Paiva R. P., Mendes T. and Cardoso A. (2006). “Melody Detection in Polyphonic Musical Signals: Exploiting Perceptual Rules, Note Salience and Melodic Smoothness”. Computer Music Journal, Vol. 30, No. 4, pp. 80-98, MIT Press.

Paiva R. P. (2007). “An Approach for Melody Extraction from Polyphonic Audio: Using Perceptual Principles and Melodic Smoothness”. Proceedings of the 154th Meeting of the Acoustical Society of America, New Orleans, USA, November 2007.

Paiva R. P. (2005). “An Algorithm for Melody Detection in Polyphonic Recordings”. Proceedings of the Music Information Retrieval Exchange – MIREX’2005.

Paiva R. P., Mendes T. and Cardoso A. (2005). “On the Detection of Melody Notes in Polyphonic Audio”. Proceedings of the International Conference on Music Information Retrieval – ISMIR’2005, London, UK.

Paiva R. P., Mendes T. and Cardoso A. (2005). “Exploiting Melodic Smoothness for Melody Detection in Polyphonic Audio”. Proceedings of the International Computer Music Conference – ICMC’2005, Barcelona, Spain.

Paiva R. P., Mendes T. and Cardoso A. (2005). “On the Definition of Musical Notes from Pitch Tracks for Melody Detection in Polyphonic Recordings”. Proceedings of the International Conference on Digital Audio Effects – DAFx’05, Madrid, Spain.

Paiva R. P., Mendes T. and Cardoso A. (2005). “Segmentation of Pitch Tracks for Melody Detection in Polyphonic Audio”. Proceedings of the European Signal Processing Conference – EUSIPCO’2005, Antalya, Turkey.

Paiva R. P., Mendes T. and Cardoso A. (2005). “An Auditory Model Based Approach for Melody Detection in Polyphonic Musical Recordings”. U. K. Wiil (ed.) Computer Music Modeling and Retrieval – CMMR 2004, Esbjerg, Denmark, Lecture Notes in Computer Science, Vol. 3310, pp. 21-40.

Paiva R. P., Mendes T. and Cardoso A. (2004). “A Methodology for Detection of Melody in Polyphonic Musical Signals”. Proceedings of the 116th Audio Engineering Society Convention – AES 116, Berlim, Germany.

Gómez E., Streich S., Ong B., Paiva R. P., Tappert S., Batke J.-M., Poliner G., Ellis D. and Bello J. P. (2006). “A Quantitative Comparison of Different Approaches for Melody Extraction from Polyphonic Audio Recordings”. Technical Report, Music Technology Group, Pompeu Fabra University, Spain.

Paiva R. P. (2007). “Melody Detection in Polyphonic Audio”. Invited Presentation. 9th Meeting of APEA - Associação Portuguesa de Engenharia Áudio (national branch of the Audio Engineering Society), Leiria, Portugal. October 20, 2007.