Computational Modelling of Perceptual Music Features
Today, computers and the Internet are commonly used for all aspects of music culture from production to listening. When a typical online music database contains 5 million songs, new computer tools are needed for characterizing, and indexing music audio. This is the focus of the new research field Music Information Retrieval (MIR).
In an earlier project, a new approach was successfully tested using perceptually defined features for describing music in a four-layered approach with audio, low-level features, mid-level perceptual features, and semantic descriptions. A new finding was that these features could be described within a theoretical framework of ecological perception. It also indicated that the computational modeling of the perceptual features was a challenging task.
In the proposed project, we will further expand the feature space using an ecological approach, for example for describing different rhythmical patterns and their relation to motion. A major focus will be the development of computational models for predicting the perceptual features using signal-processing techniques in combination with machine learning methods. Finally, we will develop small proof-of-concept applications in order to disseminate and test the result. A successful result will bridge the semantic gap between earlier studies in music psychology and contemporary data-mining projects leading to new ways of understanding and modeling music audio.
Group: Sound and Music Computing
Anders Friberg (Project leader)
Funding: VR (621-2012-4685)
Duration: 2013 - 2017
Keywords: Music information retrieval, perceptual features, computational models
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