- Music Technology and Sound Studies
- Music and Audio Processing
- Technology Use by Older Adults
- Neuroscience and Music Perception
- Innovative Approaches in Technology and Social Development
- Innovation, Technology, and Society
- Cultural Differences and Values
- Sharing Economy and Platforms
- Diverse Musicological Studies
- Technology Adoption and User Behaviour
- Mobile Health and mHealth Applications
- Speech and Audio Processing
- Musicology and Musical Analysis
- Music History and Culture
- Diverse Music Education Insights
- Social Policies and Healthcare Reform
- Social and Demographic Issues in Germany
McMaster University
2024-2025
Durham University
2025
Catholic University of Applied Sciences Freiburg
2023-2024
Research on music psychology has increased exponentially over the past half century, providing insights a wide range of topics underpinning perception, cognition, and production music. This wealth research means we are now in place to develop specific, testable theories music, with potential impact our wider understanding human biology, culture, communication. However, development more widely applicable inclusive responses requires these be informed by data that is representative global...
To successfully use information and communication technologies (ICT) for aging processes, older adults need to have a variety of skills. This study examined whether volunteering is critical resource in dealing with the motives internet use, higher self-efficacy, lower obsolescence. For this purpose, distinction was made between who perform voluntary work ICT field, outside non-volunteers. In study, 331 (mean, 70 years; range, 60–90 39% female) participated an online questionnaire were...
Great musicians have a unique style and, with training, humans can learn to distinguish between these styles. What differences performers enable us make such judgments? We investigate this question by building supervised-learning model that predicts performer identity from data extracted automatically an audio recording. Such could be trained on all kinds of musical features, but here we focus specifically rhythm, which (unlike harmony, melody, and timbre) is relevant for any instrument....
Recent advances in automatic music transcription have facilitated the creation of large databases symbolic transcriptions improvised forms including jazz, where traditional notated scores are not normally available. In conjunction with source separation models that enable audio to be “demixed” into separate signals for multiple instrument classes, these algorithms can also applied generate annotations every musician a performance. This analysis interesting performer-level and ensemble-level...
Recent advances in automatic music transcription have facilitated the creation of large databases symbolic transcriptions improvised forms including jazz, where traditional notated scores are not normally available. In conjunction with source separation models that enable audio to be “demixed” into separate signals for multiple instrument classes, these algorithms can also applied generate annotations every musician a performance. This analysis interesting performer-level and ensemble-level...
Great musicians have a unique style and, with training, humans can learn to distinguish between these styles. What differences performers enable us make such judgements? We investigate this question by building machine learning model that predicts performer identity from data extracted automatically an audio recording. Such could be trained on all kinds of musical features, but here we focus specifically rhythm, which (unlike harmony, melody and timbre) is relevant for any instrument....