Absolute of Relative? A New Approach to Building Feature Vectors For Emotion Tracking In Music
Imbrasaitė, V. & Robinson, P. (2013). Absolute of Relative? A New Approach to Building Feature Vectors For Emotion Tracking In Music. In: Proceedings of the 3rd International Conference on Music & Emotion (ICME3), Jyväskylä, Finland, 11th - 15th June 2013. Geoff Luck & Olivier Brabant (Eds.). University of Jyväskylä, Department of Music.
Date
2013It is believed that violation of or conformity to expectancy when listening to music is one of the main sources of musical emotion. To address this, we test a new way of building feature vectors and representing features within the vector for the machine learning approach to continuous emotion tracking systems. Instead of looking at the absolute values for specific features, we concentrate on the average value of that feature across the whole song and the difference between that and the absolute value for a particular sample. To test this “relative” representation, we used a corpus of popular music with continuous labels on the arousalvalence space. The model consists of a Support Vector Regression classifier for each axis, with one feature vector for each second of a song. The relative representation, when compared to the standard way of building feature vectors, gives a 10% improvement on average (and up to 25% improvement for some models) on the explained variance for both the valence and arousal axes. We also show that this result is
...
Publisher
University of Jyväskylä, Department of MusicConference
The 3rd International Conference on Music & Emotion, Jyväskylä, Finland, June 11-15, 2013Is part of publication
Proceedings of the 3rd International Conference on Music & Emotion (ICME3), Jyväskylä, Finland, 11th - 15th June 2013. Geoff Luck & Olivier Brabant (Eds.). ISBN 978-951-39-5250-1Metadata
Show full item recordCollections
- ICME 2013 [49]
License
Related items
Showing items with similar title or keywords.
-
Aberrant brain functional networks in type 2 diabetes mellitus : A graph theoretical and support-vector machine approach
Lin, Lin; Zhang, Jindi; Liu, Yutong; Hao, Xinyu; Shen, Jing; Yu, Yang; Xu, Huashuai; Cong, Fengyu; Li, Huanjie; Wu, Jianlin (Frontiers Media SA, 2022)Objective: Type 2 diabetes mellitus (T2DM) is a high risk of cognitive decline and dementia, but the underlying mechanisms are not yet clearly understood. This study aimed to explore the functional connectivity (FC) and ... -
KaKaRaKe - User-Friendly Visualization for Multiobjective Optimization with High-Dimensional Objective Vectors
Dächert, Kerstin; Klamroth, Kathrin; Miettinen, Kaisa; Steuer, Ralph E. (Dagstuhl Publishing, 2020) -
Utilization of Efficient Features, Vectors and Machine Learning for Ranking Techniques
Pandey, Gaurav (Jyväskylän yliopisto, 2019)Document ranking systems and recommender systems are two of the most used applications on the internet. Document ranking systems search for documents in response to a query given by the user. On the other hand, recommender systems ... -
Methods to extract multi-dimensional features of event-related brain activities from EEG data
Zhang, Guanghui (Jyväskylän yliopisto, 2021)Cognitive processes are studied, among others, by analyzing event-related potentials/oscillations (ERPs/EROs) with various signal processing techniques. The commonly used processing techniques have, however, various ... -
Unstable feature relevance in classification tasks
Skrypnyk, Iryna (University of Jyväskylä, 2011)