Using perceptually defined music features in music information retrieval
This addresses the challenge of improving music information retrieval for applications like emotion analysis by shifting from traditional music theory concepts to perceptual features, though it is incremental in modeling human perception.
The study tackled the problem of describing music properties based on human perception by introducing perceptual features, predicting emotion ratings with up to 90% explained variance using a small combination of these features.
In this study, the notion of perceptual features is introduced for describing general music properties based on human perception. This is an attempt at rethinking the concept of features, in order to understand the underlying human perception mechanisms. Instead of using concepts from music theory such as tones, pitches, and chords, a set of nine features describing overall properties of the music was selected. They were chosen from qualitative measures used in psychology studies and motivated from an ecological approach. The selected perceptual features were rated in two listening experiments using two different data sets. They were modeled both from symbolic (MIDI) and audio data using different sets of computational features. Ratings of emotional expression were predicted using the perceptual features. The results indicate that (1) at least some of the perceptual features are reliable estimates; (2) emotion ratings could be predicted by a small combination of perceptual features with an explained variance up to 90%; (3) the perceptual features could only to a limited extent be modeled using existing audio features. The results also clearly indicated that a small number of dedicated features were superior to a 'brute force' model using a large number of general audio features.