Ranking Based Experimental Music Emotion Recognition
Paper i proceeding, 2017

Emotion recognition is an open problem in Affective Computing the field. Music emotion recognition (MER) has challenges including variability of musical content across genres, the cultural background of listeners, reliability of ground truth data, and the modeling human hearing in computational domains. In this study, we focus on experimental music emotion recognition. First, we present a music corpus that contains 100 experimental music clips and 40 music clips from 8 musical genres. The dataset (the music clips and annotations) is publicly available at: http://metacreation.net/project/emusic/. Then, we present a crowdsourcing method that we use to collect ground truth via ranking the valence and arousal of music clips. Next, we propose a smoothed RankSVM (SRSVM) method. The evaluation has shown that the SRSVM outperforms four other ranking algorithms. Finally, we analyze the distribution of perceived emotion of experimental music against other genres to demonstrate the difference between genres.

data science

affect estimation

music information retrieval

machine learning

Författare

Jianyu Fan

Simon Fraser University

Kivanc Tatar

Data Science och AI

Miles Thorogood

University of British Columbia (UBC)

Philippe Pasquier

Simon Fraser University

Proceedings of the 18th International Society for Music Information Retrieval Conference, ISMIR 2017


978-981-11-5179-8 (ISBN)

International Society for Music Information Retrieval Conference
Suzhou, China,

Ämneskategorier

Medieteknik

Data- och informationsvetenskap

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2024-02-16