Learning with Music Signals: Technology Meets Education

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The revolution in music distribution, storage, and consumption has fueled tremendous interest in developing techniques and tools for organizing, analyzing, retrieving, and presenting music-related data. As a result, the field of music information retrieval (MIR) has matured over the last 20 years into an independent research area related to many different disciplines, including signal processing, machine learning, information retrieval, musicology, and the digital humanities. This project aims to break new ground in technology and education in these disciplines using music as a challenging and instructive multimedia domain. The project is unique in its way of approaching and exploring the concept of learning from different angles. First, learning from data, we will build on and advance recent deep learning (DL) techniques for extracting complex features and hidden relationships directly from raw music signals. Second, by learning from the experience of traditional engineering approaches, our objective is to understand better existing and to develop more interpretable DL-based systems by integrating prior knowledge in various ways. In particular, as a novel strategy with great potential, we want to transform classical model-based MIR approaches into differentiable multilayer networks, which can then be blended with DL-based techniques to form explainable hybrid models that are less vulnerable to data biases and confounding factors. Third, in collaboration with domain experts, we will consider specialized music corpora to gain a deeper understanding of both the music data and our models' behavior while exploring the potential of computational models for musicological research. Fourth, we will examine how music may serve as a motivating vehicle to make learning in technical disciplines such as signal processing or machine learning an interactive pursuit. Through our holistic approach to learning, we want to achieve significant advances in the development of explainable hybrid models and reshape how recent technology is applied and communicated in interdisciplinary research and education.