Naslov
Cochlea-based Features for Music Emotion Classification

Autori
Kraljević, Luka ; Russo, Mladen ; Mlikota, Mia ; Šarić Matko

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of the 14th International Conference on Signal Processing and Multimedia Applications 2017 / - , 2017

Skup
14th International Conference on Signal Processing and Multimedia Applications

Mjesto i datum
Madrid, Španjolska, 24.07.2017. - 26.07.2017

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Music, Emotion Detection, Cochlea, Gammatone Filterbank.

Sažetak
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 previously 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.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo

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