Cochlea-based Features for Music Emotion Classification

Luka Kraljević, Mladen Russo, Mia Mlikota, Matko Šarić
2017 Proceedings of the 14th International Joint Conference on e-Business and Telecommunications  
Listening to music often evokes strong emotions. With the rapid growth of easily-accessible digital music libraries there is an increasing need in reliable music emotion recognition systems. Common musical features like tempo, mode, pitch, clarity, etc. which can be easily calculated from audio signal are associated with particular emotions and are often used in emotion detection systems. Based on the idea that humans don't detect emotions from pure audio signal but from a signal that had been
more » ... reviously processed by the cochlea, in this work we propose new cochlear based features for music emotion recognition. Features are calculated from the gammatone filterbank model output and emotion classification is then performed using Support Vector Machine (SVM) and TreeBagger classifiers. Proposed features are evaluated on publicly available 1000 songs database and compared to other commonly used features. Results show that our approach is effective and outperforms other commonly used features. In the combined features set we achieved accuracy of 83.88% and 75.12% for arousal and valence. 64 Kraljević, L., Russo, M., Mlikota, M. and Šarić, M. Cochlea-based Features for Music Emotion Classification.
doi:10.5220/0006466900640068 dblp:conf/sigmap/KraljevicRMS17 fatcat:ihhpvcoylzco5k7rnbv2xb3vgm