Journal Articles
2018(2 publications)
[publication]Malheiro, R., and Panda, Renato and Paulo Gomes and Paiva, R.P. , "Emotionally-Relevant Features for Classification and Regression of Music Lyrics", IEEE Transactions on Affective Computing,  vol. 9,  pp. 240-254, 2018
[citation][year=2017]Çano, E., Morisio, M.. "MoodyLyrics: A  Sentiment  Annotated  Lyrics  Dataset. International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence", Hong Kong, March, 2017.

[publication]Panda, Renato and Malheiro, R., and Paiva, R.P. , "Novel audio features for music emotion recognition", IEEE Transactions on Affective Computing (early access), 2018

2015(1 publication)
[publication]Panda, Renato and Rocha, B.M.M. and Paiva, R.P. , "Music Emotion Recognition with Standard and Melodic Audio Features", Applied Artificial Intelligence,  vol. 29,  pp. 313-334, 2015
[citation][year=2016]Saim Shin, Sei-Jin Jang, Donghyun Lee, Unsang Park and Ji-Hwan Kim, "Brainwave-based Mood Classification Using Regularized Comm," KSII Transactions on Internet and Information Systems, vol. 10, no. 2, pp. 807-824, 2016. DOI: 10.3837/tiis.2016.02.020
[citation][year=2015]Dufour, I. (2015). Improving Music Mood Annotation Using Polygonal Circular Regression. MSc Thesis. Department of Computer Science, University of Victoria, Victoria, BC, Canada.

Conference Articles
2018(1 publication)
[publication]Panda, Renato and Malheiro, R., and Paiva, R.P. , "Musical Texture and Expressivity Features for Music Emotion Recognition", in 19th International Society for Music Information Retrieval Conference – ISMIR 2018, 2018

2016(2 publications)
[publication]Malheiro, R., and Panda, Renato and Paulo Gomes and Paiva, R.P. , "Classification and Regression of Music Lyrics: Emotionally-Significant Features", in 8th International Conference on Knowledge Discovery and Information Retrieval – KDIR’2016, 2016

[publication]Malheiro, R., and Panda, Renato and Paulo Gomes and Paiva, R.P. , "Bi-Modal Music Emotion Recognition: Novel Lyrical Features and Dataset", in 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, 2016
[citation][year=2017]Çano, E., Morisio, M.. "MoodyLyrics: A  Sentiment  Annotated  Lyrics  Dataset. International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence", Hong Kong, March, 2017.

2013(4 publications)
[publication]Malheiro, R., and Panda, Renato and Paulo Gomes and Paiva, R.P. , "Music Emotion Recognition from Lyrics: A Comparative Study", in 6th International Workshop on Machine Learning  and Music, 2013
[citation][year=2017]Çano, E., Morisio, M.. "MoodyLyrics: A  Sentiment  Annotated  Lyrics  Dataset. International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence", Hong Kong, March, 2017.

[publication]Rocha, B.M.M. and Panda, Renato and Paiva, R.P. , "Music Emotion Recognition: The Importance of Melodic Features", in 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, 2013
[citation][year=2016]Tomar D., Agarwal S. (2016) Multi-label Classifier for Emotion Recognition from Music. In: Nagar A., Mohapatra D., Chaki N. (eds) Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics. Smart Innovation, Systems and Technologies, vol 43. Springer, New Delhi
[citation][year=2015]Lee J, Kim D-W. Ranking Tag Pairs for Music Recommendation Using Acoustic Similarity. International Journal of Fuzzy Logic and Intelligent Systems. 2015;15(3):159-165. doi:10.5391/IJFIS.2015.15.3.159
[citation][year=2015]Sundararajoo, Kohshelan (2015) "Improvement of audio feature extraction techniques in traditional Indian string musical instrument". Masters thesis, Universiti Tun Hussein Onn Malaysia.

[publication]Panda, Renato and Rocha, B.M.M. and Paiva, R.P. , "Dimensional Music Emotion Recognition: Combining Standard and Melodic Audio Features", in 10th International Symposium on Computer Music Multidisciplinary Research – CMMR’2013, Marseille, France., 2013
[citation][year=2016]Wang, Ju-Chiang, Yi-Hsuan Yang, and Hsin-Min Wang. "Affective music information retrieval." Emotions and Personality in Personalized Services. Springer International Publishing, 2016. 227-261.
[citation][year=2016]Witteveen, Jeroen. "Predicting Relevance of Emotion Tags". MSc thesis. Faculty of Humanities, Utrecht University, 2016.
[citation][year=2016]Weihs, Claus, et al.,  "Music Data Analysis: Foundations and Applications." Taylor & Francis. (2016). ISBN: 978-1-4987-1956-8 / 978-1-4987-1957-5
[citation][year=2016]Van Balen, J. M. H. "Audio description and corpus analysis of popular music". PhD Thesis. Utrecht University, 2016.
[citation][year=2015]Vatolkin, Igor, Günter Rudolph, and Claus Weihs. "Interpretability of Music Classification as a Criterion for Evolutionary Multi-objective Feature Selection." International Conference on Evolutionary and Biologically Inspired Music and Art. Springer International Publishing, 2015.
[citation][year=2015]Dufour, I. (2015). Improving Music Mood Annotation Using Polygonal Circular Regression. MSc Thesis. Department of Computer Science, University of Victoria, Victoria, BC, Canada.

[publication]Panda, Renato and Malheiro, R., and Rocha, B.M.M. and António Oliveira and Paiva, R.P. , "Multi-Modal Music Emotion Recognition: A New Dataset, Methodology and Comparative Analysis", in 10th International Symposium on Computer Music Multidisciplinary Research – CMMR’2013, Marseille, France., 2013
[citation][year=2016]Wang, Cheng-I., Jennifer Hsu, and Shlomo Dubnov. "Machine Improvisation with Variable Markov Oracle: Toward Guided and Structured Improvisation." Computers in Entertainment (CIE) 14.3 (2016): 4.
[citation][year=2016]Ricardo Scholz, Geber Ramalho, Giordano Cabral. "Cross Task Study on MIREX Recent Results: An Index for Evolution Measurement and Some Stagnation Hypotheses". ISMIR 2016: 372-378
[citation][year=2016]Weihs, Claus, et al., "Music Data Analysis: Foundations and Applications." Taylor & Francis. (2016). ISBN: 978-1-4987-1956-8 / 978-1-4987-1957-5
[citation][year=2015]Wang, Ju-Chiang, et al. "Modeling the affective content of music with a Gaussian mixture model." IEEE Transactions on Affective Computing 6.1 (2015): 56-68.
[citation][year=2015]Ren, Jia-Min, Ming-Ju Wu, and Jyh-Shing Roger Jang. "Automatic music mood classification based on timbre and modulation features." IEEE Transactions on Affective Computing 6.3 (2015): 236-246.
[citation][year=2015]Baniya, Babu Kaji, and Choong Seon Hong. "Music Mood Classification using Reduced Audio Features." (2015): 915-917.
[citation][year=2015]WONG, C. M. (2015). "User Customization for Music Emotion Classification using Online Sequential Extreme Learning Machine". (Outstanding Academic Papers by Students (OAPS)). Retrieved from University of Macau, Outstanding Academic Papers by Students Repository.
[citation][year=2014]Sturm, Bob L. "A simple method to determine if a music information retrieval system is a “horse”." IEEE Transactions on Multimedia 16.6 (2014): 1636-1644.
[citation][year=2014]Ramamurthy, Karthikeyan Natesan, et al. "Consensus inference with multilayer graphs for multi-modal data." Signals, Systems and Computers, 2014 48th Asilomar Conference on. IEEE, 2014.

2012(3 publications)
[publication]Panda, Renato and Paiva, R.P. , "Music Emotion Classification: Analysis of a Classifier Ensemble Approach", in 5th International Workshop on Music and Machine Learning – MML’2012 – in conjunction with the 19th International Conference on Machine Learning – ICML’2012, 2012
[citation][year=2016]Saim Shin, Sei-Jin Jang, Donghyun Lee, Unsang Park and Ji-Hwan Kim, "Brainwave-based Mood Classification Using Regularized Comm," KSII Transactions on Internet and Information Systems, vol. 10, no. 2, pp. 807-824, 2016. DOI: 10.3837/tiis.2016.02.020
[citation][year=2013]1. Piva, R. (2013). Combining timbric and rhythmic features for semantic music tagging. MSc Thesis. University of Padova, Italy.

[publication]Panda, Renato and Paiva, R.P. , "Music Emotion Classification: Dataset Acquisition and Comparative Analysis", in 15th International Conference on Digital Audio Effects – DAFx’12, 2012
[citation][year=2015]Dufour, I. (2015). Improving Music Mood Annotation Using Polygonal Circular Regression. MSc Thesis. Department of Computer Science, University of Victoria, Victoria, BC, Canada.
[citation][year=2014]Baume, Chris, et al. "Selection of audio features for music emotion recognition using production music." Audio Engineering Society Conference: 53rd International Conference: Semantic Audio. Audio Engineering Society, 2014.
[citation][year=2014]Xu, Jieping, et al. "Source separation improves music emotion recognition." Proceedings of International Conference on Multimedia Retrieval. ACM, 2014.
[citation][year=2014]Lee, Jin Ha, and Xiao Hu. "Cross-cultural similarities and differences in music mood perception." iConference 2014 Proceedings (2014).
[citation][year=2014]2. da Costa, DMR. (2014, November). Effects of music preference and selection on stress management. MSc Thesis. University of Minho.
[citation][year=2013]1. Baume, C. (2013, May). Evaluation of Acoustic Features for Music Emotion Recognition. In Audio Engineering Society Convention 134. Audio Engineering Society.
[citation][year=2013]Saari, Pasi, et al. "Using semantic layer projection for enhancing music mood prediction with audio features." Sound and Music Computing Conference, Stockholm, Sweden. 2013.

[publication]Panda, Renato and Paiva, R.P. , "MIREX 2012: Mood Classifcation Task Submission", in Music Information Retrieval Evaluation eXchange - MIREX'2012, 2012
[citation][year=2013]Roberto Piva. Combining timbric and rhythmic features for semantic music tagging. Ingegneria dell'Informazione e Ingegneria Elettrica, Università di Padova. MSc Thesis (2013)

2011(3 publications)
[publication]Panda, Renato and Paiva, R.P. , "Using Support Vector Machines for Automatic Mood Tracking in Audio Music", in 130th Audio Engineering Convention - AES 130, 2011
[citation][year=2016]Chau, Chuck-jee, Ronald Mo, and Andrew Horner. "The Emotional Characteristics of Piano Sounds with Different Pitch and Dynamics." Journal of the Audio Engineering Society 64.11 (2016): 918-932.
[citation][year=2016]Aljanaki, A. (2016) "Music and emotion: representation and computational modeling" .PhD Thesis, Utrecht University. ISBN: 978-94-6328-083-9
[citation][year=2015]Dufour, I. (2015). Improving Music Mood Annotation Using Polygonal Circular Regression. MSc Thesis. Department of Computer Science, University of Victoria, Victoria, BC, Canada.
[citation][year=2015]Imbrasaite, Vaiva. "Continuous dimensional emotion tracking in music". PhD thesis. University of Cambridge, 2015.
[citation][year=2015]Plewa, M., Kostek, B. (2015) "Music Mood Visualization Using Self-Organizing Maps". Archives of Acoustics. Volume 40, Issue 4, Pages 513–525, ISSN (Online) 2300-262X, DOI: 10.1515/aoa-2015-0051, December 2015.
[citation][year=2015]C. H. Chung and H. Chen, "Vector representation of emotion flow for popular music," Multimedia Signal Processing (MMSP), 2015 IEEE 17th International Workshop on, Xiamen, 2015, pp. 1-6.
doi: 10.1109/MMSP.2015.7340797
[citation][year=2014]Imbrasait?, Vaiva, Tadas Baltrušaitis, and Peter Robinson. "CCNF for continuous emotion tracking in music: Comparison with CCRF and relative feature representation." Multimedia and Expo Workshops (ICMEW), 2014 IEEE International Conference on. IEEE, 2014.
[citation][year=2014]Baltrušaitis, Tadas. Automatic facial expression analysis. PhD thesis. University of Cambridge, 2014.
[citation][year=2013]Amanda Cohen Mostafavi, Zbigniew Ras and Alicja Wieczorkowska (2013). “Developing Personalized Classifiers for Retrieving Music by Mood”, ECML/PKDD 2013
[citation][year=2013]Kostek, Bo?ena, and Magdalena Plewa. "Parametrisation and correlation analysis applied to music mood classification." International Journal of Computational Intelligence Studies 2.1 (2013): 4-25.
[citation][year=2013]Imbrasait?, Vaiva, and Peter Robinson. "Absolute of Relative? A New Approach to Building Feature Vectors For Emotion Tracking In Music." The 3rd International Conference on Music & Emotion, Jyväskylä, Finland, June 11-15, 2013. University of Jyväskylä, Department of Music, 2013.
[citation][year=2013]Mostafavi, Amanda Cohen, Zbigniew W. Ra?, and Alicja A. Wieczorkowska. "From Personalized to Hierarchically Structured Classifiers for Retrieving Music by Mood." International Workshop on New Frontiers in Mining Complex Patterns. Springer International Publishing, 2013.
[citation][year=2013]Imbrasaite, Vaiva, Tadas Baltrušaitis, and Peter Robinson (2013). "EMOTION TRACKING IN MUSIC USING CONTINUOUS CONDITIONAL RANDOM FIELDS AND RELATIVE FEATURE REPRESENTATION.", AAM Workshop, ICME’2013. 
[citation][year=2013]Plewa, Magdalena, and Bozena Kostek. "Multidimensional Scaling Analysis Applied to Music Mood Recognition." Audio Engineering Society Convention 134. Audio Engineering Society, 2013.

[citation][year=2012]Plewa, Magdalena, and Bozena Kostek. "A Study on Correlation between Tempo and Mood of Music." Audio Engineering Society Convention 133. Audio Engineering Society, 2012.
[citation][year=2012]Scott Beveridge  (2012). “A novel approach for time-continuous tension prediction in film soundtracks”, Proceedings of the 7th Audio Mostly Conference: A Conference on Interaction with Sound, Pages 55-60
[citation][year=2012]den Brinker, Bert, Ralph van Dinther, and Janto Skowronek. "Expressed music mood classification compared with valence and arousal ratings." EURASIP Journal on Audio, Speech, and Music Processing 2012.1 (2012): 1-14.
[citation][year=2012]Plewa, Magdalena, and Bozena Kostek. "Creating Mood Dictionary Associated with Music." Audio Engineering Society Convention 132. 2012.

[citation][year=2011]1. J McGowan, "Harmonious: An Emotion-Matching System for Intelligent Use of Players’ Own Music Libraries with Game Soundtracks", Harmonious Project Technical Report, Leeds Metropolitan Universit, UK.

[publication]Panda, Renato and Paiva, R.P. , "Automatic Creation of Mood Playlists in the Thayer Plane: A Methodology and a Comparative Study", in Sound and Music Computing Conference - SMC'2011, 2011

[publication]Cardoso, L.F.A. and Panda, Renato and Paiva, R.P. , "MOODetector: A Prototype Software Tool for Mood-based Playlist Generation", in Simpósio de Informática - INForum 2011, 2011
[citation][year=2016]Dias, Ricardo, Daniel Gonçalves, and Manuel J. Fonseca. "From manual to assisted playlist creation: a survey." Multimedia Tools and Applications (2016): 1-29.
[citation][year=2015]Gajjar, Kunjal, and Siddhi Shah. "Mood based Playlist Generation for Hindi Popular Music: A Proposed Model." International Journal of Computer Applications (0975–8887) Volume (2015).
[citation][year=2014]Ramnani, Sweety, and Ravi Prakash Gorthi. "A Model to Incorporate Emotional Sensitivity into Human Computer Interactions." Proceedings of the 2014 workshop on Emotion Representation and Modelling in Human-Computer-Interaction-Systems. ACM, 2014.

PhD Theses
2019(1 publication)
[publication]Panda, Renato , "Emotion-based Analysis and Classification of Audio Music", 2019

MSc Theses
2010(1 publication)
[publication]Panda, Renato , "Automatic Mood Tracking in Audio Music", 2010
[citation][year=2013]Sivaprakasam, T., and P. Dhanalakshmi. "A Robust Environmental Sound Recognition System using Frequency Domain Features." International Journal of Computer Applications 80.9 (2013).