Music information retrieval (MIR) is a fast-growing research area. One of its aims is to extract musical characteristics from audio. In this study, we assumed the roles of researchers without further technical MIR experience and set out to test in an exploratory way its opportunities and challenges in the specific context of musical emotion perception. Twenty sound engineers rated 60 musical excerpts from a broad range of styles with respect to 22 spectral, musical, and cross-modal features (perceptual features) and perceived emotional expression. In addition, we extracted 86 features (acoustic features) of the excerpts with the MIRtoolbox (Lartillot & Toiviainen, 2007). First, we evaluated the perceptual and extracted acoustic features. Both perceptual and acoustic features posed statistical challenges (e.g., perceptual features were often bimodally distributed, and acoustic features highly correlated). Second, we tested the suitability of the acoustic features for modeling perceived emotional content. Four nearly disjunctive feature sets provided similar results, implying a certain arbitrariness of feature selection. We compared the predictive power of perceptual and acoustic features using linear mixed effects models, but the results were inconclusive. We discuss critical points and make suggestions to further evaluate MIR tools for modeling music perception and processing.
Challenges and Opportunities of Predicting Musical Emotions with Perceptual and Automatized Features
We thank Freya Materna, Claudia Lehr, Jonas Schändlinger, and Jens Fünderich for their help with data collection, and Dirk Lange, Christoph Claßen, and Hans-Joachim Maempel for establishing contact to sound engineers, as well as our participants. In addition, we thank two anonymous reviewers and the reviewer Anders Friberg for their excellent, elaborate, and thorough reviews and lively discussions throughout the review process.
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Elke B. Lange, Klaus Frieler; Challenges and Opportunities of Predicting Musical Emotions with Perceptual and Automatized Features. Music Perception 1 December 2018; 36 (2): 217–242. doi: https://doi.org/10.1525/mp.2018.36.2.217
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