Books 2018(1 publication) [publication]Malheiro, R., , "Emotions Detection in Music Lyrics using Machine Learning and Keyword-Based Approaches", vol. NA, 2018 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 2004(1 publication) [publication]Malheiro, R., and Paiva, R.P. and Antonio Jose Mendes and Mendes, T. and Amilcar Cardoso , "Sistemas de Classificação Musical com Redes Neuronais", Gestão e Desenvolvimento, vol. 12, pp. 167-195, 2004 [citation][year=2010]Barreira, L., Unsupervised automatic music genre classification. Dissertação de Mestrado, Universidade Nova de Lisboa, 2010. [citation][year=2007]Moraes R. B. (2007). “ANÁLISE E SÍNTESE DE INSTRUMENTOS MUSICAIS DE SOPRO DE MADEIRA”. MSc Thesis, Universidade Federal do Rio de Janeiro, Brazil. [citation][year=2005]Oliveira P. (2005). “Caracterização da Ocupação do Solo com Recurso à Aplicação de Modelos de Misturas Espectrais em Séries Multi-Temporais de Imagens Modis”. MSc Thesis, ISEGI, New University of Lisbon, Junho 2005. 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(3 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. [publication]Malheiro, R., and Gonçalo Oliveira, H, and Paulo Gomes and Paiva, R.P. , "Keyword-Based Approach for Lyrics Emotion Variation Detection", in 8th International Conference on Knowledge Discovery and Information Retrieval – KDIR’2016, November 2016, 2016 [citation][year=2019]Parisi, L., Francia, S., Olivastri, S., and Tavella, M. S. (2019). Exploiting synchronized lyrics and vocal features for music emotion detection. ArXiv e-prints. [citation][year=2017]Vale, P. M. F. (2017). The role of artist and genre on music emotion recognition. Master’s thesis, Universidade Nova de Lisboa. 2013(2 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]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. 2004(3 publications) [publication]Malheiro, R., and Paiva, R.P. and Antonio Jose Mendes and Mendes, T. and Amilcar Cardoso , "Classification of Recorded Classical Music using Neural Networks", in EIS'2004, 2004 [citation][year=2006]Gruijl, J., Wiering (2006). M. "Musical Instrument Classification using Democratic Liquid State Machines?, Benelearn\'06: in Proceedings of the 15th Belgian-Dutch Conference on Machine Learning, pp. 33-40, edited by Y. Saeys, E. Tsiporkova, B. De Baets, and Y. Van de Peer, 2006. [citation][year=2005]Lee et al. (2005). "Fast Panoramic Image Generation Method Using Morphological Corner Detection?. In Advances in Multimedia Information Processing " PCM 2005. [citation][year=2005]Park, D., Nguyen, D, Beack, S. e Park, S., Classification of Audio Signals Using Gradient-Based Fuzzy c-Means Algorithm with Divergence Measure. In Advances in Multimedia Information Processing " PCM 2005, LNCS, pp. 698-708, 2005. [publication]Malheiro, R., and Paiva, R.P. and Antonio Jose Mendes and Mendes, T. and Amilcar Cardoso , "A Prototype for Classification of Classical Music using Neural Networks", in ASC'2004, 2004 [citation][year=2012]1. Kalayci, I.; Korukoglu, S.; , "Classification of Turkish Maqam music using k-means algorithm and artificial neural networks," Signal Processing and Communications Applications Conference (SIU), 2012 20th , vol., no., pp.1-4, 18-20 April 2012. [citation][year=2008]1. Boulandet, Romain, et al. "How to move from perception to design: Application to keystroke sound." INTER-NOISE and NOISE-CON Congress and Conference Proceedings. Vol. 2008. No. 1. Institute of Noise Control Engineering, 2008. [citation][year=2008]Romain, B., Hervé, L., Patrick, M., Jacques, R. e Sylvain, S., How to move from perception to design: application to keystroke sound. In Proceedings of INTER-NOISE and NOISE-CON Conference. Vol. 2008. No. 2. Institute of Noise Control Engineering, 2008. [citation][year=2007]1. Alluri, V. (2007). “TOWARD AUTOMATIC MUSICOLOGICAL CLASSIFICATION OF WESTERN CLASSICAL MUSIC”. MSc Thesis, University of Miami, USA. [citation][year=2006]Ezzaidi H. and Rouat J. (2006). “Automatic Musical Genre Classification Using Divergence and Average Information Measures”. International Journal of Applied Mathematics and Computer Sciences, Vol. 3, No. 4, pp. 202 – 206. [citation][year=2006]Xin Jin, Rongfang Bie. Random Forest and PCA for Self-Organizing Maps based Automatic Music Genre Discrimination. In Proceedings of the International Conference on Data Mining - DMIN'2006, pp.414-417. [citation][year=2005]Kordos, M., Search-based algorithms for multilayer perceptrons. Tese de Doutoramento, Silesian University of Technology, Polónia, 2005. [publication]Malheiro, R., and Paiva, R.P. and Antonio Jose Mendes and Mendes, T. and Amilcar Cardoso , "Classification of Recorded Classical Music: A Methodology and a Comparative Study", in International Symposium on Brain Inspired Cognitive Systems - BICS’2004, 2004 [citation][year=2006]1. Tantini F. (2006). “Apprentissage d’automates stochastiques pour la classification automatique de styles musicaux”. Techinical Report, Jean Monnet University, Saint-Etiène, France. PhD Theses 2017(1 publication) [publication]Malheiro, R., , "Emotion-Based Analysis and Classification of Music Lyrics", 2017 MSc Theses 2004(1 publication) [publication]Malheiro, R., , "Sistemas de Classificação Automática em Géneros Musicais", 2004 Tech Report 1997(1 publication) [publication]Malheiro, R., , "Aplicação da Tecnologia Web em Quiosques Multimédia", 1997