Music Emotion Recognition: The Importance of Melodic Features



We study the importance of a melodic audio (MA) feature set in music emotion recognition (MER) and compare its performance to an approach using only standard audio (SA) features. We also analyse the fusion of both types of features. Employing only SA features, the best attained performance was 46.3%, while using only MA features the best outcome was 59.1% (Fmeasure). A combination of SA and MA features improved results to 64%. These results might have an important impact to help break the so-called glass ceiling in MER, as most current approaches are based on SA features.


music emotion recognition

Related Project

MOODetector: A System for Mood-based Classification and Retrieval of Audio Music


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, September 2013

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Year 2016 : 1 citations

 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

Year 2015 : 2 citations

 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

 Sundararajoo, Kohshelan (2015) "Improvement of audio feature extraction techniques in traditional Indian string musical instrument". Masters thesis, Universiti Tun Hussein Onn Malaysia.