Using a large dataset of digital music downloads, this thesis examines the extent to which cognitive-psychology research can generate and predict user behaviours relevant to the distinct fields of computer science and music perception. Three distinct topics are explored. Topic one describes the current difficulties with using large digital music resources for cognitive research and provides a solution by linking metadata through a complex validation process. Topic two uses this enriched information to explore the extent to which extracted acoustic features influence genre preferences considering personality, and mood research; analysis suggests acoustic features which are pronounced in an individual's preferred genre influence choice when selecting less-preferred genres. Topic three examines whether metrics of music listening behaviour can be derived and validated by social psychological research; results support the notion that user behaviours can be derived and validated using an informed psychological background, and may be more useful than acoustic features for a variety of computational music tasks. A primary motivation for this thesis was to approach interdisciplinary music research in two ways: (1) utilize a shared understanding of statistical learning as a theoretical framework underpinning for prediction and interpretation; and (2) by providing resources, and approaches to analysis of "big data" which are experimentally valid, and psychologically useful. The unique strengths of this interdisciplinary approach, and the weaknesses that remain, are then addressed by discussing refined analyses and future directions. / Thesis / Master of Science (MSc) / This thesis examines whether research from cognitive psychology can be used to inform and predict behaviours germane to computational music analysis including genre choice, music feature preference, and consumption patterns from data provided by digital-music platforms. Specific topics of focus include: information integrity and consistency of large datasets, whether signal processing algorithms can be used to assess music preference across multiple genres, and the degree to which consumption behaviours can be derived and validated using more traditional experimental paradigms. Results suggest that psychologically motivated research can provide useful insights and metrics in the computationally focused area of global music consumption behaviour and digital music analysis. Limitations that remain within this interdisciplinary approach are addressed by providing refined analysis techniques for future work.
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/23886 |
| Date | 11 1900 |
| Creators | Barone, Michael D. |
| Contributors | Woolhouse, Matthew H., Psychology |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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