Third party funded individual grant
Acronym: MU 2686/11-2 (No. 350953655)
Start date : 01.07.2022
End date : 30.06.2025
Website: https://www.audiolabs-erlangen.de/fau/professor/mueller/projects/isad2
In music information retrieval (MIR), the development of computational methods for analyzing, segmenting, and classifying music signals is of fundamental importance. In the project's first phase (2017-2020), we explored fundamental techniques for detecting characteristic sound events present in a given music recording. Here, our focus was on informed approaches that exploit musical knowledge in the form of score information, instrument samples, or musically salient sections. We considered concrete tasks such as locating audio sections with a specific timbre or instrument, identifying monophonic themes in complex polyphonic music recordings, and classifying music genres or playing styles based on melodic contours. We tested our approaches within complex music scenarios, including instrumental Western classical music, jazz, and opera recordings. In this second phase of the project, our goals are significantly extended. First, we go beyond the music scenario by considering environmental sounds as a second challenging audio domain. As a central methodology, we explore and combine the benefits of model-based and data-driven techniques to learn task-specific sound event representations. Furthermore, we investigate hierarchical approaches to simultaneously incorporate, exploit, learn, and capture sound events that manifest on different temporal scales and belong to hierarchically ordered categories. An overarching goal of the project's second phase is to develop explainable deep learning models that provide a better understanding of the structural and acoustic properties of sound events.