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Exploring Song Segmentation for Music Emotion Variation Detection
(2024-09) Ferreira, Tomas; Redinho, Hugo; Louro, Pedro L.; Malheiro, Ricardo; Paiva, Rui Pedro; Panda, Renato
This paper evaluates the impact of song segmentation on Music Emotion Variation Detection (MEVD). In particular, the All-In-One song-structure segmentation system was employed to this end and compared to a fixed 1.5-sec window approach. Acoustic features were extracted for each obtained segment/window, which were classified with SVMs. The attained results (best F1-score of 55.9%) suggest that, despite its promise, the potential of this song segmentation approach was not fully exploited, possibly due to the small employed dataset. Nevertheless, preliminary results are encouraging.